{ "results": [ { "id": "250f198a797e19282e39beff11388017", "title": "Physical Scientist (Latent Print Analyst)", "employer_name": "Department of Homeland Security", "employer_slug": "department-of-homeland-security", "location_text": "Washington, District of Columbia", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 70623, "salary_max": 91815, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 70623, "base_salary_max": 91815, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T14:58:19.000Z", "apply_url": "https://www.usajobs.gov:443/job/872775500", "apply_url_verified": false, "ats": "usajobs", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Physical Scientist (Latent Print Analyst) Washington, District of Columbia Summary Joining the Secret Service, Office of Field Operations, Forensic Service Division will allow you to safeguard the nation's financial infrastructure and protect national and visiting foreign leaders by conducting forensic examinations in support of criminal investigations and protective intelligence operations. For definitions of terms found in this announcement, please click here. Duties The selectee will serve as a Physical Scientist (Latent Print Analyst) in the Office of Field Operations, Forensic Service Division. Typical work assignments include: Performing physical science work by applying established scientific methods and standardized protocols to process, examine, and evaluate latent print evidence. Assisting in the sequential processing of evidence, carefully selecting techniques based on scientific suitability, laboratory procedures, and case parameters. Preparing technical notes, contributing to laboratory reports, and presenting scientific information to peers, supervisors and law enforcement personnel. Assisting in coordinating casework with other laboratory units to ensure the proper sequencing of examinations and compliance with technical and administrative requirements. Apply for this exciting opportunity to impact the agency's ability to identify individuals, generate investigative leads, support protective intelligence operations and provide reliable forensic results in support of national security. Requirements U.S. Citizenship is required. Males born after 12/31/1959 must be registered for Selective Service. If selected for this position, you will be required to: Obtain and maintain a Top Secret Tier 5 clearance. For more information visit OPM Mythbuster Page. Submit a credit check after a conditional offer of employment has been accepted. Submit", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [ "DC" ], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "hire_states_allowed": { "field": "hire_states_allowed", "source": "rule:state_eligibility", "db_column": "hire_states_allowed" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Defense", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": true, "security_clearance": "top_secret", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.2ae88040c038bec7fc", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "250f198a797e19282e39beff11388017", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Physical Scientist (Latent Print Analyst) Washington, District of Columbia Summary Joining the Secret Service, Office of Field Operations, Forensic Service Division will allow you to safeguard the nation's financial infrastructure and protect national and visiting foreign leaders by conducting forensic examinations in support of criminal investigations and protective intelligence operations. For definitions of terms found in this announcement, please click here. Duties The selectee will serve as a Physical Scientist (Latent Print Analyst) in the Office of Field Operations, Forensic Service Division. Typical work assignments include: Performing physical science work by applying established scientific methods and standardized protocols to process, examine, and evaluate latent print evidence. Assisting in the sequential processing of evidence, carefully selecting techniques based on scientific suitability, laboratory procedures, and case parameters. Preparing technical notes, contributing to laboratory reports, and presenting scientific information to peers, supervisors and law enforcement personnel. Assisting in coordinating casework with other laboratory units to ensure the proper sequencing of examinations and compliance with technical and administrative requirements. Apply for this exciting opportunity to impact the agency's ability to identify individuals, generate investigative leads, support protective intelligence operations and provide reliable forensic results in support of national security. Requirements U.S. Citizenship is required. Males born after 12/31/1959 must be registered for Selective Service. If selected for this position, you will be required to: Obtain and maintain a Top Secret Tier 5 clearance. For more information visit OPM Mythbuster Page. Submit a credit check after a conditional offer of employment has been accepted. Submit", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:250f198a797e19282e39beff11388017:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.title.2825a4210890a0593a", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "250f198a797e19282e39beff11388017", "signal_type": "query_match", "display_text": "Title: \"latent\"", "tooltip": "Title matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "title", "source_value": "Physical Scientist (Latent Print Analyst)", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:250f198a797e19282e39beff11388017:title:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.25432e82787f01f828", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "250f198a797e19282e39beff11388017", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "Physical Scientist (Latent Print Analyst)", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:2eaf078f2d052929cc2b1ca6", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.title.ceaa9a48ca16b0866a", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "250f198a797e19282e39beff11388017", "signal_type": "query_match", "display_text": "Title: \"latent\"", "tooltip": "Title contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "title", "source_value": "Physical Scientist (Latent Print Analyst)", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/872775500", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/872775500", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/872775500", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "hire_states_allowed": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/872775500", "source_values": [ "rule:state_eligibility" ], "checked_at": null }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/872775500", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/872775500", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "11d812ddf27180ceee47454c8d9540a2", "title": "Workplace Manager", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/bedfeef6-537c-4258-9267-19577b8d4af6", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Workplace Manager San Francisco | OnSite WORKPLACE MANAGER Latent builds robust, reliable medical intelligence that helps health systems get patients on therapy faster. We're growing quickly and we need a hands-on Workplace Manager to manage the San Francisco and New York City offices end-to-end and run the rituals that keep our team connected and moving fast. You'll operate as an operator + host + program runner: keep the space humming, stand up simple processes, and deliver high-signal, low-overhead moments (all-hands, lunches, offsites, recognition) that make hard weeks fun. OUTCOMES YOU'LL OWN - Always-ready office: Doors open, desks available, AV works, snacks stocked, guests welcomed-every day. - Smooth move-in & scale-up: Seamless build-out and migration to our new HQ; vendor programs stood up and humming. - Reliable rituals: All-hands, lunches, birthdays, and team events run on time with clear owners and clean AV. - Lightweight ops systems: Simple playbooks and trackers (facilities, vendors, supplies, visitors, IT-lite, reimbursements) that reduce chaos week over week. - High morale signals: People feel seen and supported; wins and milestones are recognized consistently. WHAT YOU'LL DO Space & Facilities - Own day-to-day San Francisco (HQ) operations (access/badging, front-of-house, visitors, mail/shipments, seating, safety). - Stand up/lead vendors: janitorial, food/beverage, plants, security, handyman, AV, coffee/water, waste/e-waste. - Manage the office move: timeline, punch list, vendors, furniture, labeling, day-of execution, decommission. - Keep an office run-sheet and rolling 90-day facilities plan (repairs, refreshes, supplies, budgets). - Track space utilization; adjust seating and phone-booth etiquette as we grow. - Manage", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "operations", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.6b7b2ff41814d89b67", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "11d812ddf27180ceee47454c8d9540a2", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Workplace Manager San Francisco | OnSite WORKPLACE MANAGER Latent builds robust, reliable medical intelligence that helps health systems get patients on therapy faster. We're growing quickly and we need a hands-on Workplace Manager to manage the San Francisco and New York City offices end-to-end and run the rituals that keep our team connected and moving fast. You'll operate as an operator + host + program runner: keep the space humming, stand up simple processes, and deliver high-signal, low-overhead moments (all-hands, lunches, offsites, recognition) that make hard weeks fun. OUTCOMES YOU'LL OWN - Always-ready office: Doors open, desks available, AV works, snacks stocked, guests welcomed-every day. - Smooth move-in & scale-up: Seamless build-out and migration to our new HQ; vendor programs stood up and humming. - Reliable rituals: All-hands, lunches, birthdays, and team events run on time with clear owners and clean AV. - Lightweight ops systems: Simple playbooks and trackers (facilities, vendors, supplies, visitors, IT-lite, reimbursements) that reduce chaos week over week. - High morale signals: People feel seen and supported; wins and milestones are recognized consistently. WHAT YOU'LL DO Space & Facilities - Own day-to-day San Francisco (HQ) operations (access/badging, front-of-house, visitors, mail/shipments, seating, safety). - Stand up/lead vendors: janitorial, food/beverage, plants, security, handyman, AV, coffee/water, waste/e-waste. - Manage the office move: timeline, punch list, vendors, furniture, labeling, day-of execution, decommission. - Keep an office run-sheet and rolling 90-day facilities plan (repairs, refreshes, supplies, budgets). - Track space utilization; adjust seating and phone-booth etiquette as we grow. - Manage", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:11d812ddf27180ceee47454c8d9540a2:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.f0043e125422066034", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "11d812ddf27180ceee47454c8d9540a2", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:11d812ddf27180ceee47454c8d9540a2:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.1bbb083bc42328a6a5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "11d812ddf27180ceee47454c8d9540a2", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:99125e57921c7186e47111fd", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.09cf44892a7fe38b43", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "11d812ddf27180ceee47454c8d9540a2", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bedfeef6-537c-4258-9267-19577b8d4af6", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bedfeef6-537c-4258-9267-19577b8d4af6", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bedfeef6-537c-4258-9267-19577b8d4af6", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bedfeef6-537c-4258-9267-19577b8d4af6", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "ec729f587c26d111c9009d28553671b5", "title": "Research Scientist", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/648fa6e1-52b8-4c43-8cf9-ab80260ecb1d", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Research Scientist San Francisco | OnSite RESEARCH SCIENTIST ABOUT LATENT HEALTH Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family. For everyone else, care is fragmented and impersonal. Medical history is scattered across systems that don't communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly-but not the individual. We believe this can be fundamentally rebuilt. At Latent Health, we are building systems that understand both: - the population (clinical knowledge at scale) - and the individual (longitudinal patient history) Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning. Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth. ML AT LATENT HEALTH The Machine Learning team is responsible for building systems that run in real clinical workflows. We work on: - Verifiable reinforcement learning at scale - Mid-training and post-training of foundation models - Novel objectives derived from longitudinal patient data We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production. We are a small team and expect engineers to take ownership of critical systems, not components. THE ROLE As a Machine Learning Engineer, Research, you will own the design and development of novel modeling approaches that advance state-of-the-art", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.8e8a0c3515ee99699c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "ec729f587c26d111c9009d28553671b5", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Research Scientist San Francisco | OnSite RESEARCH SCIENTIST ABOUT LATENT HEALTH Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family. For everyone else, care is fragmented and impersonal. Medical history is scattered across systems that don't communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly-but not the individual. We believe this can be fundamentally rebuilt. At Latent Health, we are building systems that understand both: - the population (clinical knowledge at scale) - and the individual (longitudinal patient history) Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning. Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth. ML AT LATENT HEALTH The Machine Learning team is responsible for building systems that run in real clinical workflows. We work on: - Verifiable reinforcement learning at scale - Mid-training and post-training of foundation models - Novel objectives derived from longitudinal patient data We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production. We are a small team and expect engineers to take ownership of critical systems, not components. THE ROLE As a Machine Learning Engineer, Research, you will own the design and development of novel modeling approaches that advance state-of-the-art", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:ec729f587c26d111c9009d28553671b5:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.0745ecc962721aa2ab", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "ec729f587c26d111c9009d28553671b5", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:ec729f587c26d111c9009d28553671b5:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.7d4a955f9feb19e310", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "ec729f587c26d111c9009d28553671b5", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:a0a000c96525b45faf8d7589", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.d1a8fc23054e6e81c2", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "ec729f587c26d111c9009d28553671b5", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.1b56c437d04d1ba8e4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "ec729f587c26d111c9009d28553671b5", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "engineering", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:ec729f587c26d111c9009d28553671b5:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/648fa6e1-52b8-4c43-8cf9-ab80260ecb1d", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/648fa6e1-52b8-4c43-8cf9-ab80260ecb1d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/648fa6e1-52b8-4c43-8cf9-ab80260ecb1d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/648fa6e1-52b8-4c43-8cf9-ab80260ecb1d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "6b75f5ecf6f2de87b8e7dcde9d6d307c", "title": "Product Operations & Support Associate", "employer_name": "Latent", "employer_slug": "latent", "location_text": "Remote", "country": "unknown", "employment_type": "unknown", "remote_status": "remote", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 0, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-07T17:58:24.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/cb474c7f-2197-474b-8c8c-53dc5f328cc9", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Product Operations & Support Associate Remote PRODUCT OPERATIONS & SUPPORT ASSOCIATE ABOUT LATENT HEALTH Latent Health is building the AI nervous system for healthcare. Our platform powers faster clinical workflows, reduces administrative burden, and unlocks operational and financial performance for health systems through safe, explainable, and scalable applied LLM technology. We partner with leading hospital systems to improve patient outcomes while reducing friction across care teams and operational leaders. We're a fast-growing Series A company backed by world-class investors. If you thrive in ambiguity, move with urgency, and care deeply about improving healthcare at scale, you'll love building at Latent. ABOUT THE ROLE As a Product Operations & Support Associate, you will serve as a frontline member of our Customer Enablement team, delivering empathetic, efficient, and high-quality support to our users. This is a highly visible, fast-paced role ideal for a proactive problem-solver who can manage multiple priorities, navigate technical complexity, and ensure a seamless support experience. This role requires coverage during standard business hours across both Eastern and Central time zones. Candidates should be available to work shifts aligned with approximately 8:00 AM - 6:00 PM EST / 7:00 AM - 5:00 PM CST, with some flexibility based on team and customer needs. WHAT YOU'LL DO - Serve as the first point of contact for customer inquiries, delivering accurate, empathetic, and timely resolutions across technical and product-related issues (AI output quality, application access, authentication, error logs, etc.). - Manage multiple competing priorities while maintaining strong responsiveness, reliability, and accountability", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 51, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "product", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": "CST", "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.6ed162a2dbea224fbd", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b75f5ecf6f2de87b8e7dcde9d6d307c", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Product Operations & Support Associate Remote PRODUCT OPERATIONS & SUPPORT ASSOCIATE ABOUT LATENT HEALTH Latent Health is building the AI nervous system for healthcare. Our platform powers faster clinical workflows, reduces administrative burden, and unlocks operational and financial performance for health systems through safe, explainable, and scalable applied LLM technology. We partner with leading hospital systems to improve patient outcomes while reducing friction across care teams and operational leaders. We're a fast-growing Series A company backed by world-class investors. If you thrive in ambiguity, move with urgency, and care deeply about improving healthcare at scale, you'll love building at Latent. ABOUT THE ROLE As a Product Operations & Support Associate, you will serve as a frontline member of our Customer Enablement team, delivering empathetic, efficient, and high-quality support to our users. This is a highly visible, fast-paced role ideal for a proactive problem-solver who can manage multiple priorities, navigate technical complexity, and ensure a seamless support experience. This role requires coverage during standard business hours across both Eastern and Central time zones. Candidates should be available to work shifts aligned with approximately 8:00 AM - 6:00 PM EST / 7:00 AM - 5:00 PM CST, with some flexibility based on team and customer needs. WHAT YOU'LL DO - Serve as the first point of contact for customer inquiries, delivering accurate, empathetic, and timely resolutions across technical and product-related issues (AI output quality, application access, authentication, error logs, etc.). - Manage multiple competing priorities while maintaining strong responsiveness, reliability, and accountability", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6b75f5ecf6f2de87b8e7dcde9d6d307c:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.7cfedc607f403ce0f2", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b75f5ecf6f2de87b8e7dcde9d6d307c", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6b75f5ecf6f2de87b8e7dcde9d6d307c:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.210af19891ce233acf", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b75f5ecf6f2de87b8e7dcde9d6d307c", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:e33a58b9db943c607eab0b59", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.6bff2f7b9b727b742d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b75f5ecf6f2de87b8e7dcde9d6d307c", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.2e162f4651080160f8", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b75f5ecf6f2de87b8e7dcde9d6d307c", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "product", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6b75f5ecf6f2de87b8e7dcde9d6d307c:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/cb474c7f-2197-474b-8c8c-53dc5f328cc9", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/cb474c7f-2197-474b-8c8c-53dc5f328cc9", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/cb474c7f-2197-474b-8c8c-53dc5f328cc9", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/cb474c7f-2197-474b-8c8c-53dc5f328cc9", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "b318d36a537d5c981a20bc0d4e4961ff", "title": "Recruiting Coordinator", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/e8fe169f-312f-4227-972b-f4223f149e5f", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Recruiting Coordinator San Francisco | OnSite Our Philosophy At Latent, we believe that exploring career possibilities at a new company should be an experience, not just a process. People are the key to success for any organization, and our Talent team sits at the heart of that mission. We are one of the most visible teams in the company-partnering across every function, offering feedback, and supporting every person here. We know that who we hire has an outsized impact on our future success, which makes every interaction in our recruiting process matter. The Role As a Recruiting Coordinator at Latent, you will be the architect of the candidate experience. From the moment a candidate is invited to interview, their journey is yours to design. You'll coordinate with hiring leads, communicate directly with candidates, manage scheduling logistics, and ensure that every interview day runs seamlessly. On the day of interviews, you'll be the go-to expert with all the answers-keeping things running smoothly and ensuring each candidate feels welcomed, supported, and respected. Beyond execution, you'll also research and implement new ways to elevate our recruiting process-constantly thinking creatively and iterating on how we create exceptional experiences. You will: - Own the planning and execution of candidate interview days - Partner with hiring managers and interviewers to ensure alignment and preparedness - Communicate proactively with candidates to guide them through the process - Manage all scheduling and logistics with precision and care - Identify opportunities to improve our recruiting experience and implement new", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "hr", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.8588669b5d7beaac70", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b318d36a537d5c981a20bc0d4e4961ff", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Recruiting Coordinator San Francisco | OnSite Our Philosophy At Latent, we believe that exploring career possibilities at a new company should be an experience, not just a process. People are the key to success for any organization, and our Talent team sits at the heart of that mission. We are one of the most visible teams in the company-partnering across every function, offering feedback, and supporting every person here. We know that who we hire has an outsized impact on our future success, which makes every interaction in our recruiting process matter. The Role As a Recruiting Coordinator at Latent, you will be the architect of the candidate experience. From the moment a candidate is invited to interview, their journey is yours to design. You'll coordinate with hiring leads, communicate directly with candidates, manage scheduling logistics, and ensure that every interview day runs seamlessly. On the day of interviews, you'll be the go-to expert with all the answers-keeping things running smoothly and ensuring each candidate feels welcomed, supported, and respected. Beyond execution, you'll also research and implement new ways to elevate our recruiting process-constantly thinking creatively and iterating on how we create exceptional experiences. You will: - Own the planning and execution of candidate interview days - Partner with hiring managers and interviewers to ensure alignment and preparedness - Communicate proactively with candidates to guide them through the process - Manage all scheduling and logistics with precision and care - Identify opportunities to improve our recruiting experience and implement new", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:b318d36a537d5c981a20bc0d4e4961ff:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.7d58429ffe3091823c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b318d36a537d5c981a20bc0d4e4961ff", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:b318d36a537d5c981a20bc0d4e4961ff:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.5a76dfdce9ddca5a54", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b318d36a537d5c981a20bc0d4e4961ff", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:9c15c8d1777ddb9bee7c7527", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.d00a33729e279c54b0", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b318d36a537d5c981a20bc0d4e4961ff", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.e00a5f97e3d67fb85c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b318d36a537d5c981a20bc0d4e4961ff", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "hr", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:b318d36a537d5c981a20bc0d4e4961ff:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e8fe169f-312f-4227-972b-f4223f149e5f", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e8fe169f-312f-4227-972b-f4223f149e5f", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e8fe169f-312f-4227-972b-f4223f149e5f", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e8fe169f-312f-4227-972b-f4223f149e5f", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "2d69c71e742fc5016d8e3b089821580f", "title": "Senior People Operations Generalist", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/dbbcd010-b65e-40e6-8580-c7ea95d57251", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior People Operations Generalist San Francisco | OnSite THE ROLE Latent is the leading clinical AI platform for medication access, partnering with over 45 health systems to streamline prior authorizations, 340B compliance, and medication appeals. We're accelerating time-to-therapy for patients while reducing the administrative burden on the clinicians and staff who care for them. We're looking for a sharp, flexible Senior People Operations Generalist to help us build the foundational programs that help our small team and the company scale. This is an early and highly visible role on our People team, in which you'll own the day-to-day operations of HR, including benefits, onboarding, immigration, compliance, and more. You'll work closely with team leadership and cross-functional stakeholders to build the systems and programs that foster an incredible employee experience. This role is based in San Francisco. It will be a great fit for someone who thrives in ambiguity, takes initiative, brings order to chaos, and is genuinely excited by the idea of building People infrastructure at a company that's on a clear trajectory. WHAT YOU'LL DO Benefits & Perks Administration - Administer and continuously improve our benefits programs, serving as the primary point of contact for day-to-day employee support and vendor operations. - Support benefit enrollments alongside our broker, including employee communications, vendor coordination, and data accuracy in our HRIS. - Measure and iterate on employee feedback to continue to hone a competitive and highly utilized benefits and perks program. Onboarding & Employee Lifecycle - Own the onboarding experience from", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "hr", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.0e839af22e8a956759", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2d69c71e742fc5016d8e3b089821580f", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior People Operations Generalist San Francisco | OnSite THE ROLE Latent is the leading clinical AI platform for medication access, partnering with over 45 health systems to streamline prior authorizations, 340B compliance, and medication appeals. We're accelerating time-to-therapy for patients while reducing the administrative burden on the clinicians and staff who care for them. We're looking for a sharp, flexible Senior People Operations Generalist to help us build the foundational programs that help our small team and the company scale. This is an early and highly visible role on our People team, in which you'll own the day-to-day operations of HR, including benefits, onboarding, immigration, compliance, and more. You'll work closely with team leadership and cross-functional stakeholders to build the systems and programs that foster an incredible employee experience. This role is based in San Francisco. It will be a great fit for someone who thrives in ambiguity, takes initiative, brings order to chaos, and is genuinely excited by the idea of building People infrastructure at a company that's on a clear trajectory. WHAT YOU'LL DO Benefits & Perks Administration - Administer and continuously improve our benefits programs, serving as the primary point of contact for day-to-day employee support and vendor operations. - Support benefit enrollments alongside our broker, including employee communications, vendor coordination, and data accuracy in our HRIS. - Measure and iterate on employee feedback to continue to hone a competitive and highly utilized benefits and perks program. Onboarding & Employee Lifecycle - Own the onboarding experience from", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:2d69c71e742fc5016d8e3b089821580f:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.3ca7a7c49c9e1df3af", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2d69c71e742fc5016d8e3b089821580f", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:2d69c71e742fc5016d8e3b089821580f:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.d8a861f164d38d275c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2d69c71e742fc5016d8e3b089821580f", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:c8570f382c4822cb6900004c", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.a362a70923b437b1bc", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2d69c71e742fc5016d8e3b089821580f", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.4f6ed96c43dd985e2a", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2d69c71e742fc5016d8e3b089821580f", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "hr", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:2d69c71e742fc5016d8e3b089821580f:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/dbbcd010-b65e-40e6-8580-c7ea95d57251", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/dbbcd010-b65e-40e6-8580-c7ea95d57251", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/dbbcd010-b65e-40e6-8580-c7ea95d57251", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/dbbcd010-b65e-40e6-8580-c7ea95d57251", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "852cb099fc6e90855bdb0d64683e40c7", "title": "Product Design", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite | New York City", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/da59c838-1f12-4318-9fe2-fa04680e2456", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Product Design San Francisco | OnSite | New York City ABOUT LATENT Latent is building the intelligence infrastructure for healthcare. Our clinical AI platform helps health systems streamline medication access by automating complex workflows - prior authorizations, appeals, and 340B compliance. By surfacing relevant clinical evidence from EHR data, we reduce administrative burden and accelerate time-to-therapy for patients. We recently raised an $80M Series A co-led by Spark Capital and Transformation Capital, with participation from General Catalyst, McKesson Ventures, Y Combinator, and Conviction. We serve 2M+ patients across 45+ of the nation's leading health systems - including Yale New Haven, Mount Sinai, UCSF, Ochsner, and Vanderbilt. We move fast, operate with high ownership, and build products that directly improve patient care. We're looking for a Product Designer to shape the next generation of clinical AI products powering healthcare. THE ROLE As we build the design function at Latent, You'll join a growing design function and own design end to end, from early problem framing through shipped product. You'll work directly with engineering, product, and customers to design enterprise software that makes complex clinical workflows feel simple. WHAT YOU'LL DO Product Design & Ownership - Own the full design process from discovery through delivery for core product areas - Make smart tradeoffs between speed and polish to keep shipping without sacrificing craft Enterprise & Workflow Design - Design complex, multi-user workflows for clinical and operational teams at large health systems - Translate dense healthcare processes into clear, intuitive product experiences -", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "product", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.82c43d5a71b7be9013", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "852cb099fc6e90855bdb0d64683e40c7", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Product Design San Francisco | OnSite | New York City ABOUT LATENT Latent is building the intelligence infrastructure for healthcare. Our clinical AI platform helps health systems streamline medication access by automating complex workflows - prior authorizations, appeals, and 340B compliance. By surfacing relevant clinical evidence from EHR data, we reduce administrative burden and accelerate time-to-therapy for patients. We recently raised an $80M Series A co-led by Spark Capital and Transformation Capital, with participation from General Catalyst, McKesson Ventures, Y Combinator, and Conviction. We serve 2M+ patients across 45+ of the nation's leading health systems - including Yale New Haven, Mount Sinai, UCSF, Ochsner, and Vanderbilt. We move fast, operate with high ownership, and build products that directly improve patient care. We're looking for a Product Designer to shape the next generation of clinical AI products powering healthcare. THE ROLE As we build the design function at Latent, You'll join a growing design function and own design end to end, from early problem framing through shipped product. You'll work directly with engineering, product, and customers to design enterprise software that makes complex clinical workflows feel simple. WHAT YOU'LL DO Product Design & Ownership - Own the full design process from discovery through delivery for core product areas - Make smart tradeoffs between speed and polish to keep shipping without sacrificing craft Enterprise & Workflow Design - Design complex, multi-user workflows for clinical and operational teams at large health systems - Translate dense healthcare processes into clear, intuitive product experiences -", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:852cb099fc6e90855bdb0d64683e40c7:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.3168c21344be25cdea", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "852cb099fc6e90855bdb0d64683e40c7", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:852cb099fc6e90855bdb0d64683e40c7:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.8e68c023d47a537db5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "852cb099fc6e90855bdb0d64683e40c7", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:f98fcb03b2173c0c231b6202", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.9206f6935645c8b52a", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "852cb099fc6e90855bdb0d64683e40c7", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.55054c2e85bd09f63e", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "852cb099fc6e90855bdb0d64683e40c7", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "product", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:852cb099fc6e90855bdb0d64683e40c7:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/da59c838-1f12-4318-9fe2-fa04680e2456", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/da59c838-1f12-4318-9fe2-fa04680e2456", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/da59c838-1f12-4318-9fe2-fa04680e2456", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/da59c838-1f12-4318-9fe2-fa04680e2456", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "184e67955a10761f4ca75d6e81a7b355", "title": "Product Manager", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite | New York City", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/9f1e2942-d911-47b1-a860-3a98e2e4e107", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Product Manager San Francisco | OnSite | New York City ABOUT LATENT Latent is building the intelligence infrastructure for healthcare. Our clinical AI platform helps health systems streamline medication access by automating complex workflows - prior authorizations, appeals, and 340B compliance. By surfacing relevant clinical evidence from EHR data, we reduce administrative burden and accelerate time-to-therapy for patients. We recently raised a $60M Series A at a $600M valuation, backed by Conviction, Spark Capital and other leading investors. We 10x'd revenue last year and now work with 40+ of the nation's leading health systems - including Mount Sinai, UCSF, Yale, Ochsner, and Mayo Clinic. We move fast, operate with high ownership, and build products that directly improve patient care. We're looking for a Product Manager to define and build the next generation of clinical AI products powering healthcare. THE ROLE As a Product Manager at Latent, you will own critical product initiatives from strategy through execution. Working closely with engineering, design, GTM, and customers, you'll identify the highest-impact problems in healthcare operations and deliver scalable solutions for large health systems. WHAT YOU'LL DO Product Strategy & Ownership - Define product strategy and roadmap aligned with company goals and customer needs - Translate ambiguous problems into clear product plans, requirements, and deliverables - Own product outcomes and metrics for the areas of the platform you lead Customer Insight & Discovery - Work directly with healthcare customers to understand clinical workflows and operational challenges - Conduct user interviews and synthesize feedback into", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "product", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.edba6d45539f49041d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "184e67955a10761f4ca75d6e81a7b355", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Product Manager San Francisco | OnSite | New York City ABOUT LATENT Latent is building the intelligence infrastructure for healthcare. Our clinical AI platform helps health systems streamline medication access by automating complex workflows - prior authorizations, appeals, and 340B compliance. By surfacing relevant clinical evidence from EHR data, we reduce administrative burden and accelerate time-to-therapy for patients. We recently raised a $60M Series A at a $600M valuation, backed by Conviction, Spark Capital and other leading investors. We 10x'd revenue last year and now work with 40+ of the nation's leading health systems - including Mount Sinai, UCSF, Yale, Ochsner, and Mayo Clinic. We move fast, operate with high ownership, and build products that directly improve patient care. We're looking for a Product Manager to define and build the next generation of clinical AI products powering healthcare. THE ROLE As a Product Manager at Latent, you will own critical product initiatives from strategy through execution. Working closely with engineering, design, GTM, and customers, you'll identify the highest-impact problems in healthcare operations and deliver scalable solutions for large health systems. WHAT YOU'LL DO Product Strategy & Ownership - Define product strategy and roadmap aligned with company goals and customer needs - Translate ambiguous problems into clear product plans, requirements, and deliverables - Own product outcomes and metrics for the areas of the platform you lead Customer Insight & Discovery - Work directly with healthcare customers to understand clinical workflows and operational challenges - Conduct user interviews and synthesize feedback into", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:184e67955a10761f4ca75d6e81a7b355:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.8ffe4c0fdc344be96d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "184e67955a10761f4ca75d6e81a7b355", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:184e67955a10761f4ca75d6e81a7b355:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.5c65488c75d83e76f4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "184e67955a10761f4ca75d6e81a7b355", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:896aa9ad37151c8629af6516", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.9808dc717d11fb86aa", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "184e67955a10761f4ca75d6e81a7b355", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.343d31c98268a2c5f4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "184e67955a10761f4ca75d6e81a7b355", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "product", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:184e67955a10761f4ca75d6e81a7b355:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/9f1e2942-d911-47b1-a860-3a98e2e4e107", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/9f1e2942-d911-47b1-a860-3a98e2e4e107", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/9f1e2942-d911-47b1-a860-3a98e2e4e107", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/9f1e2942-d911-47b1-a860-3a98e2e4e107", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "b35b3a0826455d5fc4384a5185ffafee", "title": "Creative Brand Lead", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | Hybrid | Remote", "country": "US", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/a3328583-c451-4540-8d19-a686ebfb96eb", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Creative Brand Lead New York City | Hybrid | Remote Make Latent the most memorable brand in healthcare. Scale taste and creative systems. ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 45+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. What makes us different: we engineer creative the way engineers ship software. THE ROLE We are working on a very important brand fresh. We need someone to support and take it from hand-off and run it into every surface Latent touches. Web, campaigns, social, events, video, decks, recruiting. One job. Make every Latent asset unmistakable at first glance. This role reports to the VP of Brand. The most important part: you'll do this on top of an AI creative workflow we've already built, the", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "marketing", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.27fc9022f448e3ab1f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b35b3a0826455d5fc4384a5185ffafee", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Creative Brand Lead New York City | Hybrid | Remote Make Latent the most memorable brand in healthcare. Scale taste and creative systems. ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 45+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. What makes us different: we engineer creative the way engineers ship software. THE ROLE We are working on a very important brand fresh. We need someone to support and take it from hand-off and run it into every surface Latent touches. Web, campaigns, social, events, video, decks, recruiting. One job. Make every Latent asset unmistakable at first glance. This role reports to the VP of Brand. The most important part: you'll do this on top of an AI creative workflow we've already built, the", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:b35b3a0826455d5fc4384a5185ffafee:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.256260bbc7e9e808d7", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b35b3a0826455d5fc4384a5185ffafee", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:b35b3a0826455d5fc4384a5185ffafee:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.a862af5539214db7b5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b35b3a0826455d5fc4384a5185ffafee", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:383930db6401adf03379e0c4", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.2f3c2c8fca5bdd08b0", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b35b3a0826455d5fc4384a5185ffafee", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.50a5e6b3779b83d0fa", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b35b3a0826455d5fc4384a5185ffafee", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "marketing", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:b35b3a0826455d5fc4384a5185ffafee:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/a3328583-c451-4540-8d19-a686ebfb96eb", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/a3328583-c451-4540-8d19-a686ebfb96eb", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/a3328583-c451-4540-8d19-a686ebfb96eb", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "e7f05dab2a15320f176015582df46590", "title": "Customer Enablement Trainer", "employer_name": "Latent", "employer_slug": "latent", "location_text": "Remote | New York City | San Francisco", "country": "US", "employment_type": "unknown", "remote_status": "remote", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 0, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/1cd2fb4d-928b-482d-8393-068055f5a615", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Customer Enablement Trainer Remote | New York City | San Francisco ABOUT LATENT HEALTH Latent Health is building the AI nervous system for healthcare. Our platform powers faster clinical workflows, reduces administrative burden, and unlocks operational and financial performance for health systems through safe, explainable, and scalable applied LLM technology. We partner with leading hospital systems to deliver better patient outcomes with less friction - returning valuable time back to clinicians, care teams, and operational leaders. We're a fast-growing Series A company backed by world-class investors. If you thrive in ambiguity, move fast, and care deeply about improving healthcare at scale, you'll love building at Latent. ABOUT THE ROLE As a Customer Enablement Trainer, you'll play a critical role in activating customers and accelerating time-to-value. You'll design and deliver impactful training experiences that translate Latent's AI-powered platform into intuitive, real-world clinical and operational workflows. You'll work directly with clinicians, pharmacists, operational leaders, and IT teams - helping them confidently adopt Latent and drive measurable outcomes. This role sits at the intersection of product, implementation, and customer success, partnering closely with Implementation, Account Management, Sales/Partnerships, Product, and Clinical teams. WHAT YOU'LL DO TRAINING DELIVERY & FACILITATION - Lead live virtual and onsite training sessions across clinical and operational user groups - Deliver role-based enablement that drives proficiency and reinforces best practices - Facilitate hands-on product walkthroughs, Q&A sessions, and office hours - Own go-live training execution to ensure customer readiness and successful launches CURRICULUM & CONTENT DEVELOPMENT - Create engaging training", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 51, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "teaching_education", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.37cf4f730e62ff6801", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "e7f05dab2a15320f176015582df46590", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Customer Enablement Trainer Remote | New York City | San Francisco ABOUT LATENT HEALTH Latent Health is building the AI nervous system for healthcare. Our platform powers faster clinical workflows, reduces administrative burden, and unlocks operational and financial performance for health systems through safe, explainable, and scalable applied LLM technology. We partner with leading hospital systems to deliver better patient outcomes with less friction - returning valuable time back to clinicians, care teams, and operational leaders. We're a fast-growing Series A company backed by world-class investors. If you thrive in ambiguity, move fast, and care deeply about improving healthcare at scale, you'll love building at Latent. ABOUT THE ROLE As a Customer Enablement Trainer, you'll play a critical role in activating customers and accelerating time-to-value. You'll design and deliver impactful training experiences that translate Latent's AI-powered platform into intuitive, real-world clinical and operational workflows. You'll work directly with clinicians, pharmacists, operational leaders, and IT teams - helping them confidently adopt Latent and drive measurable outcomes. This role sits at the intersection of product, implementation, and customer success, partnering closely with Implementation, Account Management, Sales/Partnerships, Product, and Clinical teams. WHAT YOU'LL DO TRAINING DELIVERY & FACILITATION - Lead live virtual and onsite training sessions across clinical and operational user groups - Deliver role-based enablement that drives proficiency and reinforces best practices - Facilitate hands-on product walkthroughs, Q&A sessions, and office hours - Own go-live training execution to ensure customer readiness and successful launches CURRICULUM & CONTENT DEVELOPMENT - Create engaging training", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:e7f05dab2a15320f176015582df46590:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.5a66da5f57eb6d5404", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "e7f05dab2a15320f176015582df46590", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:e7f05dab2a15320f176015582df46590:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.b323cdc1bb9d31cd41", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "e7f05dab2a15320f176015582df46590", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:3fb03eb77cac3da418826c94", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.d2f8eb62bc65ba566b", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "e7f05dab2a15320f176015582df46590", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.e793bf031242ae36d4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "e7f05dab2a15320f176015582df46590", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "teaching_education", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:e7f05dab2a15320f176015582df46590:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/1cd2fb4d-928b-482d-8393-068055f5a615", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/1cd2fb4d-928b-482d-8393-068055f5a615", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/1cd2fb4d-928b-482d-8393-068055f5a615", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/1cd2fb4d-928b-482d-8393-068055f5a615", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "216745e6ece50ac3de8b181fa077c6c9", "title": "Senior Events Lead", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/5134c9ec-d457-436a-927e-dabd2570a7cc", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Events Lead New York City | OnSite ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 50+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. We treat marketing and creative like engineering. THE ROLE The next phase of Latent happens in person. The right dinners with the right pharmacy leaders. A presence at HIMSS, ViVE, and Becker's that makes the category notice. Flagship events the industry talks about for a year afterward. That's the job. Decide which rooms Latent should be in, own them when we're there, and turn every event into pipeline. A role that fuses event strategy, brand-forward execution, customer relationships, and demand gen. This role reports to the VP of Brand. Based in New York with the team. WHAT YOU'LL OWN", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "marketing", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.50243dc853bfebb6b9", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "216745e6ece50ac3de8b181fa077c6c9", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior Events Lead New York City | OnSite ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 50+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. We treat marketing and creative like engineering. THE ROLE The next phase of Latent happens in person. The right dinners with the right pharmacy leaders. A presence at HIMSS, ViVE, and Becker's that makes the category notice. Flagship events the industry talks about for a year afterward. That's the job. Decide which rooms Latent should be in, own them when we're there, and turn every event into pipeline. A role that fuses event strategy, brand-forward execution, customer relationships, and demand gen. This role reports to the VP of Brand. Based in New York with the team. WHAT YOU'LL OWN", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:216745e6ece50ac3de8b181fa077c6c9:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.ba347cad8e887fb916", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "216745e6ece50ac3de8b181fa077c6c9", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:216745e6ece50ac3de8b181fa077c6c9:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.82057decf572be4893", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "216745e6ece50ac3de8b181fa077c6c9", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:8b20eedf293bd5587492d575", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.550aafbebb8e745353", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "216745e6ece50ac3de8b181fa077c6c9", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.2ee03a4b45df7eb310", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "216745e6ece50ac3de8b181fa077c6c9", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "marketing", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:216745e6ece50ac3de8b181fa077c6c9:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/5134c9ec-d457-436a-927e-dabd2570a7cc", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/5134c9ec-d457-436a-927e-dabd2570a7cc", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/5134c9ec-d457-436a-927e-dabd2570a7cc", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/5134c9ec-d457-436a-927e-dabd2570a7cc", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "f0eaa196fd6ec73c73069e51f1ec86bc", "title": "Machine Learning Engineer", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/233bbaac-d589-49f4-a8eb-6bb16673b060", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Engineer San Francisco | OnSite MACHINE LEARNING ENGINEER ABOUT LATENT HEALTH Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family. For everyone else, care is fragmented and impersonal. Medical history is scattered across systems that don't communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly-but not the individual. We believe this can be fundamentally rebuilt. At Latent Health, we are building systems that understand both: - the population (clinical knowledge at scale) - and the individual (longitudinal patient history) Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning. Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth. ML AT LATENT HEALTH The Machine Learning team is responsible for building systems that run in real clinical workflows. We work on: - Verifiable reinforcement learning at scale - Mid-training and post-training of foundation models - Novel objectives derived from longitudinal patient data We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production. We are a small team and expect engineers to take ownership of critical systems, not components. THE ROLE As a Machine Learning Engineer, you will own the design, development, and operation of production-grade ML systems that", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.1b0d3794d822411728", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f0eaa196fd6ec73c73069e51f1ec86bc", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Machine Learning Engineer San Francisco | OnSite MACHINE LEARNING ENGINEER ABOUT LATENT HEALTH Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family. For everyone else, care is fragmented and impersonal. Medical history is scattered across systems that don't communicate. Physicians have minutes to understand decades of context. And when something goes wrong, patients are left with tools that understand medicine broadly-but not the individual. We believe this can be fundamentally rebuilt. At Latent Health, we are building systems that understand both: - the population (clinical knowledge at scale) - and the individual (longitudinal patient history) Our models are designed to answer complex clinical questions with patient-specific context and verifiable reasoning. Our dataset represents one of the most clinically diverse populations in the United States, including patients with chronic illness and complex disease. Each patient record contains extraordinary depth. ML AT LATENT HEALTH The Machine Learning team is responsible for building systems that run in real clinical workflows. We work on: - Verifiable reinforcement learning at scale - Mid-training and post-training of foundation models - Novel objectives derived from longitudinal patient data We are a small group of researchers and engineers focused on pushing the frontier while shipping real systems into production. We are a small team and expect engineers to take ownership of critical systems, not components. THE ROLE As a Machine Learning Engineer, you will own the design, development, and operation of production-grade ML systems that", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f0eaa196fd6ec73c73069e51f1ec86bc:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.ffd1b627378c432636", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f0eaa196fd6ec73c73069e51f1ec86bc", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f0eaa196fd6ec73c73069e51f1ec86bc:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.035f1eb222d4a66b81", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f0eaa196fd6ec73c73069e51f1ec86bc", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:05a5fbb03f66a99c6a5849e7", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.a4d16de09268f35c39", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f0eaa196fd6ec73c73069e51f1ec86bc", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.0e305fb3dc25b8cf6d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f0eaa196fd6ec73c73069e51f1ec86bc", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "engineering", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f0eaa196fd6ec73c73069e51f1ec86bc:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/233bbaac-d589-49f4-a8eb-6bb16673b060", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/233bbaac-d589-49f4-a8eb-6bb16673b060", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/233bbaac-d589-49f4-a8eb-6bb16673b060", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/233bbaac-d589-49f4-a8eb-6bb16673b060", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "98dfe1cb7b05893ec1e30995199c5f62", "title": "Business Recruiter", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | OnSite | San Francisco", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/e5c86943-2648-4bc9-aa2a-4c92d5f5d10b", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Business Recruiter New York City | OnSite | San Francisco OUR PHILOSOPHY At Latent, we believe exploring career opportunities should feel like an experience, not just a process. People are at the center of everything we do, and our Talent team sits at the heart of that mission. We're one of the most visible teams in the company partnering across every function, supporting every person here, and shaping the future of the company through the people we hire. We know that who we hire has an outsized impact on our success. That's why this role matters so much. THE ROLE Latent Health is experiencing rapid growth, and we're seeking a GTM / Business Recruiter to help us build world-class teams across Sales, Customer Success, Marketing, Partnerships, and selected Business Operations roles. This position is based in NYC (Flatiron) and requires in-office presence as needed. You will report to the Lead GTM Recruiter, working closely on hiring strategy while taking ownership of full-cycle recruiting execution from start to finish. In this role, you'll help shape the GTM recruiting function, build talent pipelines, and ensure every candidate interaction embodies Latent's values and mission. This is a high-impact position: the talent you recruit will have a direct effect on revenue growth, market expansion, and Latent Health's ability to achieve its ambitious mission. WHAT YOU'LL DO BUILD AND SCALE GTM HIRING - Own full-cycle recruiting for GTM roles (Sales, Customer Success, Marketing, Partnerships, and select Business Ops roles) - Partner with hiring managers to", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "hr", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.95d02d7e724fb2e9df", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "98dfe1cb7b05893ec1e30995199c5f62", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Business Recruiter New York City | OnSite | San Francisco OUR PHILOSOPHY At Latent, we believe exploring career opportunities should feel like an experience, not just a process. People are at the center of everything we do, and our Talent team sits at the heart of that mission. We're one of the most visible teams in the company partnering across every function, supporting every person here, and shaping the future of the company through the people we hire. We know that who we hire has an outsized impact on our success. That's why this role matters so much. THE ROLE Latent Health is experiencing rapid growth, and we're seeking a GTM / Business Recruiter to help us build world-class teams across Sales, Customer Success, Marketing, Partnerships, and selected Business Operations roles. This position is based in NYC (Flatiron) and requires in-office presence as needed. You will report to the Lead GTM Recruiter, working closely on hiring strategy while taking ownership of full-cycle recruiting execution from start to finish. In this role, you'll help shape the GTM recruiting function, build talent pipelines, and ensure every candidate interaction embodies Latent's values and mission. This is a high-impact position: the talent you recruit will have a direct effect on revenue growth, market expansion, and Latent Health's ability to achieve its ambitious mission. WHAT YOU'LL DO BUILD AND SCALE GTM HIRING - Own full-cycle recruiting for GTM roles (Sales, Customer Success, Marketing, Partnerships, and select Business Ops roles) - Partner with hiring managers to", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:98dfe1cb7b05893ec1e30995199c5f62:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.29487ad959ced3c42f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "98dfe1cb7b05893ec1e30995199c5f62", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:98dfe1cb7b05893ec1e30995199c5f62:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.24b5cb49c98036ad60", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "98dfe1cb7b05893ec1e30995199c5f62", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:4083fa2dd30c378e9b98bf23", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.168dd86c122927cacd", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "98dfe1cb7b05893ec1e30995199c5f62", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.6d0dd1bcb8266bc2c0", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "98dfe1cb7b05893ec1e30995199c5f62", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "hr", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:98dfe1cb7b05893ec1e30995199c5f62:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e5c86943-2648-4bc9-aa2a-4c92d5f5d10b", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e5c86943-2648-4bc9-aa2a-4c92d5f5d10b", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e5c86943-2648-4bc9-aa2a-4c92d5f5d10b", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e5c86943-2648-4bc9-aa2a-4c92d5f5d10b", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "6b7d235b61e0ae3db18b697885cff15d", "title": "Sales Development Representative", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite | New York City", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/c07f4217-20a4-4dd6-9d10-45514a652955", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Sales Development Representative San Francisco | OnSite | New York City About Latent Latent is reimagining the future of medication access by using medical language models and AI to slash the burden of prior authorizations, appeals, and operational overhead across health systems. Our flagship product dives into EHR data to surface critical patient evidence and streamline authorization workflows. - Backed by Conviction (Sarah Guo), General Catalyst, Y Combinator - 25+ leading US health systems customers signed to-date including Ochsner, Yale New Haven, Vanderbilt, Mount Sinai, MetroHealth, and UCSF - ~ 4-month sales cycles vs. 18 months averages in sector At Latent, you get a rare chance to wear a tech sales hat and make a meaningful impact on healthcare. You'll report to Director of Sales. Role Overview As a Sales Development Representative (SDR) at Latent, you will be on the front lines of our growth engine. Your mission is to identify, research, and engage prospective health system customers - creating qualified opportunities for our Enterprise Account Executives. You'll develop a strong understanding of healthcare operations and AI technology, master consultative prospecting skills, and play a key role in driving awareness of Latent's mission. This role is a launchpad for future growth into closing roles (AE). Responsibilities & Impact - Research target accounts (IDNs, academic systems, regional health systems) and identify key decision-makers. - Generate pipeline through outbound prospecting (email, phone, LinkedIn, industry events) and qualify inbound leads. - Partner with Enterprise AEs to schedule meetings, build account plans, and advance", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "sales", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "masters", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.e952f6d5256cccc24b", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b7d235b61e0ae3db18b697885cff15d", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Sales Development Representative San Francisco | OnSite | New York City About Latent Latent is reimagining the future of medication access by using medical language models and AI to slash the burden of prior authorizations, appeals, and operational overhead across health systems. Our flagship product dives into EHR data to surface critical patient evidence and streamline authorization workflows. - Backed by Conviction (Sarah Guo), General Catalyst, Y Combinator - 25+ leading US health systems customers signed to-date including Ochsner, Yale New Haven, Vanderbilt, Mount Sinai, MetroHealth, and UCSF - ~ 4-month sales cycles vs. 18 months averages in sector At Latent, you get a rare chance to wear a tech sales hat and make a meaningful impact on healthcare. You'll report to Director of Sales. Role Overview As a Sales Development Representative (SDR) at Latent, you will be on the front lines of our growth engine. Your mission is to identify, research, and engage prospective health system customers - creating qualified opportunities for our Enterprise Account Executives. You'll develop a strong understanding of healthcare operations and AI technology, master consultative prospecting skills, and play a key role in driving awareness of Latent's mission. This role is a launchpad for future growth into closing roles (AE). Responsibilities & Impact - Research target accounts (IDNs, academic systems, regional health systems) and identify key decision-makers. - Generate pipeline through outbound prospecting (email, phone, LinkedIn, industry events) and qualify inbound leads. - Partner with Enterprise AEs to schedule meetings, build account plans, and advance", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6b7d235b61e0ae3db18b697885cff15d:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.42307900a46a76b883", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b7d235b61e0ae3db18b697885cff15d", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6b7d235b61e0ae3db18b697885cff15d:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.64c6aac918fc092f20", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b7d235b61e0ae3db18b697885cff15d", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:700a390831252018477e1e0c", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.67d5fad1d77f089bf4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b7d235b61e0ae3db18b697885cff15d", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.17600534c85bea7e5c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6b7d235b61e0ae3db18b697885cff15d", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "sales", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6b7d235b61e0ae3db18b697885cff15d:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c07f4217-20a4-4dd6-9d10-45514a652955", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c07f4217-20a4-4dd6-9d10-45514a652955", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c07f4217-20a4-4dd6-9d10-45514a652955", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c07f4217-20a4-4dd6-9d10-45514a652955", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "f20921f8d4f1a0d7510deb252ea92b5c", "title": "Enterprise Account Executive", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | Hybrid | San Francisco | Remote", "country": "US", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/76969fef-8f76-4cd1-bef3-cc0e0222eb2e", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Enterprise Account Executive New York City | Hybrid | San Francisco | Remote Job Title: Enterprise Account Executive Location & Travel: Onsite in New York City or San Francisco, California (3 days/week in office), travel ~30% ABOUT LATENT Latent is reimagining the future of medication access by using medical language models and AI to eliminate the burden of prior authorizations, appeals, and operational overhead across health systems. Our platform dives into EHR data to surface critical patient evidence and streamline authorization workflows-reducing delays and improving patient outcomes. - Series A company with $80M in funding, backed by Conviction (Sarah Guo), General Catalyst, and Y Combinator - 25+ leading US health systems including Ochsner, Yale New Haven, Vanderbilt, Mount Sinai, MetroHealth, and UCSF - ~4-5 month sales cycles vs. 18-month industry average At Latent, you get a rare opportunity to operate at the intersection of AI, healthcare, and enterprise sales-owning meaningful deals that directly impact patient care. ROLE OVERVIEW As an Enterprise Account Executive at Latent Health, you will own the full sales motion-from prospect identification through close and expansion-within complex health systems (IDNs, academic medical centers, and regional providers). You'll sell into highly matrixed organizations, building relationships across C-suite and senior leadership, including CMOs, CPOs, CIOs, Heads of Pharmacy, Operations, Compliance, and IT. Success in this role requires the ability to navigate clinical, technical, financial, and operational stakeholders simultaneously. You'll be responsible for threading together compelling narratives that tie ROI, clinical outcomes, operational efficiency, and AI-driven innovation into a cohesive", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "sales", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": 30, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.eb62a938a43bef0125", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f20921f8d4f1a0d7510deb252ea92b5c", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Enterprise Account Executive New York City | Hybrid | San Francisco | Remote Job Title: Enterprise Account Executive Location & Travel: Onsite in New York City or San Francisco, California (3 days/week in office), travel ~30% ABOUT LATENT Latent is reimagining the future of medication access by using medical language models and AI to eliminate the burden of prior authorizations, appeals, and operational overhead across health systems. Our platform dives into EHR data to surface critical patient evidence and streamline authorization workflows-reducing delays and improving patient outcomes. - Series A company with $80M in funding, backed by Conviction (Sarah Guo), General Catalyst, and Y Combinator - 25+ leading US health systems including Ochsner, Yale New Haven, Vanderbilt, Mount Sinai, MetroHealth, and UCSF - ~4-5 month sales cycles vs. 18-month industry average At Latent, you get a rare opportunity to operate at the intersection of AI, healthcare, and enterprise sales-owning meaningful deals that directly impact patient care. ROLE OVERVIEW As an Enterprise Account Executive at Latent Health, you will own the full sales motion-from prospect identification through close and expansion-within complex health systems (IDNs, academic medical centers, and regional providers). You'll sell into highly matrixed organizations, building relationships across C-suite and senior leadership, including CMOs, CPOs, CIOs, Heads of Pharmacy, Operations, Compliance, and IT. Success in this role requires the ability to navigate clinical, technical, financial, and operational stakeholders simultaneously. You'll be responsible for threading together compelling narratives that tie ROI, clinical outcomes, operational efficiency, and AI-driven innovation into a cohesive", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f20921f8d4f1a0d7510deb252ea92b5c:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.37303fbae95ba03116", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f20921f8d4f1a0d7510deb252ea92b5c", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f20921f8d4f1a0d7510deb252ea92b5c:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.b1ddd9d432ea54eac5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f20921f8d4f1a0d7510deb252ea92b5c", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:8179ac01ae9276932075b159", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.5bfb552d1d49a266e1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f20921f8d4f1a0d7510deb252ea92b5c", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.716910093ad2450d3b", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f20921f8d4f1a0d7510deb252ea92b5c", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "sales", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f20921f8d4f1a0d7510deb252ea92b5c:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/76969fef-8f76-4cd1-bef3-cc0e0222eb2e", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/76969fef-8f76-4cd1-bef3-cc0e0222eb2e", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/76969fef-8f76-4cd1-bef3-cc0e0222eb2e", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/76969fef-8f76-4cd1-bef3-cc0e0222eb2e", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "8c114cab70a119fbd9a6c167037772d6", "title": "Senior Product Marketing Manager", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | Hybrid | Remote", "country": "US", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/49828d69-881c-4431-be8b-bcd7455aa069", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Product Marketing Manager New York City | Hybrid | Remote Own how every release reaches the market. Make our launch machinery feel inevitable. ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 45+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. We're rewriting the playbook, not bolting AI onto a 2018 one. THE ROLE We're hiring a Senior Product Marketing Manager to own how every product reaches the market. We have a category to shape, a roadmap to time, partners to co-market with, analysts to brief, and a sales team that needs a sharp narrative. One job: make Latent's launch machinery feel inevitable to the category. You'll own positioning, packaging, launch, and partner go-to-market. This role reports to the VP of Brand. PMM kicks", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "marketing", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.813f42acfb90f464f5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8c114cab70a119fbd9a6c167037772d6", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior Product Marketing Manager New York City | Hybrid | Remote Own how every release reaches the market. Make our launch machinery feel inevitable. ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 45+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. We're rewriting the playbook, not bolting AI onto a 2018 one. THE ROLE We're hiring a Senior Product Marketing Manager to own how every product reaches the market. We have a category to shape, a roadmap to time, partners to co-market with, analysts to brief, and a sales team that needs a sharp narrative. One job: make Latent's launch machinery feel inevitable to the category. You'll own positioning, packaging, launch, and partner go-to-market. This role reports to the VP of Brand. PMM kicks", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8c114cab70a119fbd9a6c167037772d6:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.663112012cb0f82363", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8c114cab70a119fbd9a6c167037772d6", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8c114cab70a119fbd9a6c167037772d6:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.d516a11181bb4b2038", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8c114cab70a119fbd9a6c167037772d6", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:1811fe4846636d3a217d808b", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.c164eeeaf7e36221f5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8c114cab70a119fbd9a6c167037772d6", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.22f6f3efef42cede26", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8c114cab70a119fbd9a6c167037772d6", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "marketing", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8c114cab70a119fbd9a6c167037772d6:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/49828d69-881c-4431-be8b-bcd7455aa069", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/49828d69-881c-4431-be8b-bcd7455aa069", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/49828d69-881c-4431-be8b-bcd7455aa069", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/49828d69-881c-4431-be8b-bcd7455aa069", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "4edefbb7131bd8c5059531b0abff3def", "title": "Account Manager - Health Systems", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | Hybrid | San Francisco | Remote", "country": "US", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/05905486-4f89-489c-8fcd-d3fb70206fc8", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Account Manager - Health Systems New York City | Hybrid | San Francisco | Remote ROLE OVERVIEW As an Account Manager at Latent Health, you will own the post-implementation success of our health system customers, operating with deep customer obsession and a strong sense of ownership. You'll be accountable for driving adoption, delivering measurable clinical and operational outcomes, and clearly demonstrating ROI by tying product usage to real-world impact. Healthcare is fundamentally relationship-driven. Success in this role requires building trusted, long-term partnerships across complex, matrixed health systems-resulting in satisfied, referenceable customers who advocate for Latent's impact. You'll thrive in ambiguity, operate independently, and be energized by transforming healthcare through customer advocacy. This is a hybrid role based in New York City or San Francisco, with an expectation to be in the office three days per week. KEY RESPONSIBILITIES - Build and maintain strong executive and operational relationships that drive trust, retention, and long-term customer advocacy across health system accounts - Own the post-go-live customer relationship, partnering across clinical, pharmacy, operational, IT, and executive stakeholders while navigating complex governance structures - Drive adoption across departments and use cases by defining, tracking, and communicating outcomes tied to quality, efficiency, patient experience, and ROI - Proactively identify and mitigate risks related to change management, resourcing, and workflow alignment - Own renewals by ensuring customer value is clear, measurable, and aligned with executive priorities - Identify and advance upsell and cross-sell opportunities by aligning additional capabilities to customer needs and demonstrated outcomes - Partner", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "sales", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.f91a6b0402aaebcd50", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4edefbb7131bd8c5059531b0abff3def", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Account Manager - Health Systems New York City | Hybrid | San Francisco | Remote ROLE OVERVIEW As an Account Manager at Latent Health, you will own the post-implementation success of our health system customers, operating with deep customer obsession and a strong sense of ownership. You'll be accountable for driving adoption, delivering measurable clinical and operational outcomes, and clearly demonstrating ROI by tying product usage to real-world impact. Healthcare is fundamentally relationship-driven. Success in this role requires building trusted, long-term partnerships across complex, matrixed health systems-resulting in satisfied, referenceable customers who advocate for Latent's impact. You'll thrive in ambiguity, operate independently, and be energized by transforming healthcare through customer advocacy. This is a hybrid role based in New York City or San Francisco, with an expectation to be in the office three days per week. KEY RESPONSIBILITIES - Build and maintain strong executive and operational relationships that drive trust, retention, and long-term customer advocacy across health system accounts - Own the post-go-live customer relationship, partnering across clinical, pharmacy, operational, IT, and executive stakeholders while navigating complex governance structures - Drive adoption across departments and use cases by defining, tracking, and communicating outcomes tied to quality, efficiency, patient experience, and ROI - Proactively identify and mitigate risks related to change management, resourcing, and workflow alignment - Own renewals by ensuring customer value is clear, measurable, and aligned with executive priorities - Identify and advance upsell and cross-sell opportunities by aligning additional capabilities to customer needs and demonstrated outcomes - Partner", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:4edefbb7131bd8c5059531b0abff3def:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.a9795aeea525733f86", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4edefbb7131bd8c5059531b0abff3def", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:4edefbb7131bd8c5059531b0abff3def:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.157c1a2aaaef972e0c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4edefbb7131bd8c5059531b0abff3def", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:8bedd0574e1b94e15fa56fc3", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.c43e55557ffeafd4c2", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4edefbb7131bd8c5059531b0abff3def", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/05905486-4f89-489c-8fcd-d3fb70206fc8", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/05905486-4f89-489c-8fcd-d3fb70206fc8", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/05905486-4f89-489c-8fcd-d3fb70206fc8", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "d7efe96ba120586e2fa34d6c600063f6", "title": "Implementation Project Manager - 340b", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | OnSite", "country": "US", "employment_type": "contract", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/e29af78d-df97-4511-b11a-7e93def049d7", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Implementation Project Manager - 340b New York City | OnSite ABOUT LATENT HEALTH Latent Health is redefining how healthcare systems unlock the power of clinical data. Through our AI-powered platform, we streamline clinical workflows, reduce clinician burden, and improve health system efficiency. We partner with leading hospitals and health systems to deliver fast, reliable, and high-impact implementations that transform patient care. ROLE OVERVIEW The Implementation Project Manager - 340B leads end-to-end onboarding and deployment projects for health systems adopting Latent's 340b product. This role requires strong healthcare project management experience combined with a working knowledge of 340B program compliance, pharmacy operations, split-billing systems, and contract pharmacy environments. You will drive timelines, technical coordination, stakeholder alignment, and operational readiness across pharmacy, compliance, IT, revenue cycle, and executive leadership teams. You will ensure customers go live efficiently, achieve measurable value quickly, and are set up for long-term operational and compliance success. KEY RESPONSIBILITIES PROJECT LEADERSHIP & 340B CUSTOMER DELIVERY - Lead full-cycle implementation projects - from kickoff through go-live and stabilization - across health systems, with a focus on 340B program environments. - Build and manage detailed project plans inclusive of 340B-specific workflows, compliance checkpoints, and audit-readiness considerations. - Facilitate cross-functional meetings with pharmacy leadership, 340B program administrators, compliance teams, IT, and clinical operations. - Serve as the primary project point of contact, ensuring exceptional communication and alignment across stakeholders. - Identify risks specific to 340B environments (e.g., diversion risk, duplicate discount risk, contract ph", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "operations", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.2b8c8b54a3676cec56", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "d7efe96ba120586e2fa34d6c600063f6", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Implementation Project Manager - 340b New York City | OnSite ABOUT LATENT HEALTH Latent Health is redefining how healthcare systems unlock the power of clinical data. Through our AI-powered platform, we streamline clinical workflows, reduce clinician burden, and improve health system efficiency. We partner with leading hospitals and health systems to deliver fast, reliable, and high-impact implementations that transform patient care. ROLE OVERVIEW The Implementation Project Manager - 340B leads end-to-end onboarding and deployment projects for health systems adopting Latent's 340b product. This role requires strong healthcare project management experience combined with a working knowledge of 340B program compliance, pharmacy operations, split-billing systems, and contract pharmacy environments. You will drive timelines, technical coordination, stakeholder alignment, and operational readiness across pharmacy, compliance, IT, revenue cycle, and executive leadership teams. You will ensure customers go live efficiently, achieve measurable value quickly, and are set up for long-term operational and compliance success. KEY RESPONSIBILITIES PROJECT LEADERSHIP & 340B CUSTOMER DELIVERY - Lead full-cycle implementation projects - from kickoff through go-live and stabilization - across health systems, with a focus on 340B program environments. - Build and manage detailed project plans inclusive of 340B-specific workflows, compliance checkpoints, and audit-readiness considerations. - Facilitate cross-functional meetings with pharmacy leadership, 340B program administrators, compliance teams, IT, and clinical operations. - Serve as the primary project point of contact, ensuring exceptional communication and alignment across stakeholders. - Identify risks specific to 340B environments (e.g., diversion risk, duplicate discount risk, contract ph", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:d7efe96ba120586e2fa34d6c600063f6:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.cc4e7b64613bf04ac1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "d7efe96ba120586e2fa34d6c600063f6", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:d7efe96ba120586e2fa34d6c600063f6:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.3d37119bde2cc58416", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "d7efe96ba120586e2fa34d6c600063f6", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:9bc5bc1a4e2d44f88ad1a636", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.5acea0ddcbd6137f03", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "d7efe96ba120586e2fa34d6c600063f6", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e29af78d-df97-4511-b11a-7e93def049d7", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e29af78d-df97-4511-b11a-7e93def049d7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e29af78d-df97-4511-b11a-7e93def049d7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e29af78d-df97-4511-b11a-7e93def049d7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "526017073006c4c04a5e252908d79c5b", "title": "Senior Customer-Led Growth Manager", "employer_name": "Latent", "employer_slug": "latent", "location_text": "New York City | Hybrid | Remote", "country": "US", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/e1ee3c3b-cd90-4493-9c3b-62f3caf200e7", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Customer-Led Growth Manager New York City | Hybrid | Remote Turn 45+ partnerships into incredible customer case studies. Run the campaigns that convert that proof into demand and pipeline. ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 45+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. What makes us different: we treat marketing and creative like engineering. THE ROLE We've built 45+ partnerships across the top health systems in America. Faster medication access, more patients served, denials dropping. The wins are happening. The stories haven't caught up. That's the job. Turn our customer base into the most cited proof bank in healthcare AI, and run the campaigns that convert that proof into pipeline. A hybrid role between customer marketing and demand gen. This", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "marketing", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.44e3afd0e681354731", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "526017073006c4c04a5e252908d79c5b", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior Customer-Led Growth Manager New York City | Hybrid | Remote Turn 45+ partnerships into incredible customer case studies. Run the campaigns that convert that proof into demand and pipeline. ABOUT LATENT Latent Health is the leading pharmacy intelligence platform. We're an AI company changing healthcare from the inside. More patients getting their medications. Pharmacy operations that finally make economic sense. Work that used to take days, now done in minutes. We're live in 45+ health systems, including 50% of the top 20: Yale, Ochsner, Vanderbilt, UCSF, Mount Sinai. Over 2 million patients a year access medications faster because of us. Denials down 30%+. Clinicians serving 2x more patients. At Ochsner, pharmacists processing 96% more per FTE, every month. We've raised $80M from Spark Capital, Transformation Capital, McKesson Ventures, Conviction, General Catalyst, and Y Combinator. Our marketing team is AI-native to the core. Every operator gets the latest models on day one, and we run advanced AI workflows that let a small team ship at a scale and pace traditional companies can't match. What makes us different: we treat marketing and creative like engineering. THE ROLE We've built 45+ partnerships across the top health systems in America. Faster medication access, more patients served, denials dropping. The wins are happening. The stories haven't caught up. That's the job. Turn our customer base into the most cited proof bank in healthcare AI, and run the campaigns that convert that proof into pipeline. A hybrid role between customer marketing and demand gen. This", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:526017073006c4c04a5e252908d79c5b:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.0f63d8a16de5e28d78", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "526017073006c4c04a5e252908d79c5b", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:526017073006c4c04a5e252908d79c5b:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.employer_name.45648100d24a3247af", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "526017073006c4c04a5e252908d79c5b", "signal_type": "query_match", "display_text": "Employer: semantic match", "tooltip": "Employer provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:b3605eaaac316b739fb5ab57", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.480d706b407f57f9a2", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "526017073006c4c04a5e252908d79c5b", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.f6addc93f7a441532c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "526017073006c4c04a5e252908d79c5b", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "marketing", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:526017073006c4c04a5e252908d79c5b:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e1ee3c3b-cd90-4493-9c3b-62f3caf200e7", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e1ee3c3b-cd90-4493-9c3b-62f3caf200e7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e1ee3c3b-cd90-4493-9c3b-62f3caf200e7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/e1ee3c3b-cd90-4493-9c3b-62f3caf200e7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "61bec2d8271fc2082d64091bd642a8c8", "title": "Senior Low Latency Engineer", "employer_name": "Citigroup Inc.", "employer_slug": "citigroup", "location_text": "LONDON, United Kingdom", "country": "GB", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "profit_share" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-12T10:25:24.000Z", "apply_url": "https://citi.eightfold.ai/careers/job/859035294410", "apply_url_verified": false, "ats": "eightfold", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Low Latency Engineer LONDON, United Kingdom", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/831001/000083100126000011/c-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/831001/000083100126000011/c-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.55115971bacd1418ff", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "61bec2d8271fc2082d64091bd642a8c8", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "Senior Low Latency Engineer", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:23f3e4113c0d48108a5722b1", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.ebf2b9e9a40e7ccb8c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "61bec2d8271fc2082d64091bd642a8c8", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:61bec2d8271fc2082d64091bd642a8c8:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/831001/000083100126000011/c-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/831001/000083100126000011/c-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://citi.eightfold.ai/careers/job/859035294410", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://citi.eightfold.ai/careers/job/859035294410", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "680f5820241d3384e0944ce6c1d8b6a0", "title": "Software Development Engineer, GenAI", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Toronto, Ontario, CAN", "country": "CA", "employment_type": "internship", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 114800, "salary_max": 191800, "salary_min_source": null, "salary_max_source": null, "salary_currency": "CAD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 114800, "base_salary_max": 191800, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": true, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Development Engineer, GenAI Toronto, Ontario, CAN Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X org within Amazon Advertising aims to democratize access to high-quality creative assets, including copy, images and video, by building and productizing generative AI-driven tools for advertisers. We are investing in latent-diffusion and DiT models, LLMs, computer vision, reinforcement learning, and image + video synthesis. The solutions we develop will be deployed for use by self-service advertisers and agencies, as well as available to premium brands that advertise on Amazon. We are seeking an experienced Software Development Engineer who is adept at a variety of skills, that enable the productization of Generative AI experiences for advertising at scale. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, productize, deploy at scale, and bring innovative production-grade experiences to raise", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 56, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.0e0b0f7f25d9ed7b51", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "680f5820241d3384e0944ce6c1d8b6a0", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Development Engineer, GenAI Toronto, Ontario, CAN Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X org within Amazon Advertising aims to democratize access to high-quality creative assets, including copy, images and video, by building and productizing generative AI-driven tools for advertisers. We are investing in latent-diffusion and DiT models, LLMs, computer vision, reinforcement learning, and image + video synthesis. The solutions we develop will be deployed for use by self-service advertisers and agencies, as well as available to premium brands that advertise on Amazon. We are seeking an experienced Software Development Engineer who is adept at a variety of skills, that enable the productization of Generative AI experiences for advertising at scale. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, productize, deploy at scale, and bring innovative production-grade experiences to raise", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:680f5820241d3384e0944ce6c1d8b6a0:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.description_excerpt.ebabd268b229568624", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "680f5820241d3384e0944ce6c1d8b6a0", "signal_type": "query_match", "display_text": "Description: semantic match", "tooltip": "Description provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "description_excerpt", "source_value": "Software Development Engineer, GenAI Toronto, Ontario, CAN Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X org within Amazon Advertising aims to democratize access to high-quality creative assets, including copy, images and video, by building and productizing generative AI-driven tools for advertisers. We are investing in latent-diffusion and DiT models, LLMs, computer vision, reinforcement learning, and image + video synthesis. The solutions we develop will be deployed for use by self-service advertisers and agencies, as well as available to premium brands that advertise on Amazon. We are seeking an experienced Software Development Engineer who is adept at a variety of skills, that enable the productization of Generative AI experiences for advertising at scale. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, productize, deploy at scale, and bring innovative production-grade experiences to raise", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:811bd5a1faf7bd9b794018b8", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.7412417e07169a71d3", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "680f5820241d3384e0944ce6c1d8b6a0", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Development Engineer, GenAI Toronto, Ontario, CAN Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X org within Amazon Advertising aims to democratize access to high-quality creative assets, including copy, images and video, by building and productizing generative AI-driven tools for advertisers. We are investing in latent-diffusion and DiT models, LLMs, computer vision, reinforcement learning, and image + video synthesis. The solutions we develop will be deployed for use by self-service advertisers and agencies, as well as available to premium brands that advertise on Amazon. We are seeking an experienced Software Development Engineer who is adept at a variety of skills, that enable the productization of Generative AI experiences for advertising at scale. Every member of the team is expected to build customer (advertiser) facing features, contribute to the collaborative spirit within the team, productize, deploy at scale, and bring innovative production-grade experiences to raise", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "fertility_family_building_benefits": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "adoption_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "childcare_subsidy": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10417546/software-development-engineer-genai", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "1b21fc8e414a5052cd0e937bfccc1271", "title": "Applied Science Manager GenAI, CreativeX, Amazon Advertising", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Seattle, Washington, USA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 183800, "salary_max": 248700, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 183800, "base_salary_max": 248700, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-04-06T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Applied Science Manager GenAI, CreativeX, Amazon Advertising Seattle, Washington, USA Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (images, videos) by building AI-driven solutions for advertisers. To accomplish this, we are investing in latent-diffusion models, large language models (LLM), computer vision (CV), reinforced learning (RL), and image + video and audio synthesis. We are looking for a talented Applied Science Manager who can help set the scientific agenda and provide best in class mentoring within the scope of advancing advertiser-facing generative AI. You will help lead scientists within a close-knit team who is highly collaborative and at the top of their respective fields. Our science team is adept at a variety of skills, especially with agentic AI, generative multi-modal models, LLMs, computer vision, latent diffusion and related foundational models. As a Science Manager you will:", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 56, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 9, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.984520349a7ee08b5c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "1b21fc8e414a5052cd0e937bfccc1271", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Applied Science Manager GenAI, CreativeX, Amazon Advertising Seattle, Washington, USA Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (images, videos) by building AI-driven solutions for advertisers. To accomplish this, we are investing in latent-diffusion models, large language models (LLM), computer vision (CV), reinforced learning (RL), and image + video and audio synthesis. We are looking for a talented Applied Science Manager who can help set the scientific agenda and provide best in class mentoring within the scope of advancing advertiser-facing generative AI. You will help lead scientists within a close-knit team who is highly collaborative and at the top of their respective fields. Our science team is adept at a variety of skills, especially with agentic AI, generative multi-modal models, LLMs, computer vision, latent diffusion and related foundational models. As a Science Manager you will:", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:1b21fc8e414a5052cd0e937bfccc1271:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.description_excerpt.f8d03a502edfe4090d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "1b21fc8e414a5052cd0e937bfccc1271", "signal_type": "query_match", "display_text": "Description: semantic match", "tooltip": "Description provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "description_excerpt", "source_value": "Applied Science Manager GenAI, CreativeX, Amazon Advertising Seattle, Washington, USA Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (images, videos) by building AI-driven solutions for advertisers. To accomplish this, we are investing in latent-diffusion models, large language models (LLM), computer vision (CV), reinforced learning (RL), and image + video and audio synthesis. We are looking for a talented Applied Science Manager who can help set the scientific agenda and provide best in class mentoring within the scope of advancing advertiser-facing generative AI. You will help lead scientists within a close-knit team who is highly collaborative and at the top of their respective fields. Our science team is adept at a variety of skills, especially with agentic AI, generative multi-modal models, LLMs, computer vision, latent diffusion and related foundational models. As a Science Manager you will:", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:d0a333d7114cd14325e53dba", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.f47ecb1ea660e813fa", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "1b21fc8e414a5052cd0e937bfccc1271", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Applied Science Manager GenAI, CreativeX, Amazon Advertising Seattle, Washington, USA Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (images, videos) by building AI-driven solutions for advertisers. To accomplish this, we are investing in latent-diffusion models, large language models (LLM), computer vision (CV), reinforced learning (RL), and image + video and audio synthesis. We are looking for a talented Applied Science Manager who can help set the scientific agenda and provide best in class mentoring within the scope of advancing advertiser-facing generative AI. You will help lead scientists within a close-knit team who is highly collaborative and at the top of their respective fields. Our science team is adept at a variety of skills, especially with agentic AI, generative multi-modal models, LLMs, computer vision, latent diffusion and related foundational models. As a Science Manager you will:", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "fertility_family_building_benefits": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "adoption_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "childcare_subsidy": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10384255/applied-science-manager-genai-creativex-amazon-advertising", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "b075d13d549a619b2c2af6f19c030570", "title": "(Senior) Mathematiker:in / Aktuar:in* – Betriebliche Altersvorsorge – In Voll- und Teilzeit", "employer_name": "Marsh McLennan", "employer_slug": "marsh-mclennan", "location_text": "5 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T00:00:00.000Z", "apply_url": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Dsseldorf---Peter-Mller/XMLNAME--Senior--Mathematiker-in---Aktuar-in----Betriebliche-Altersvorsorge---In-Voll--und-Teilzeit_R_338415", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "(Senior) Mathematiker:in / Aktuar:in* – Betriebliche Altersvorsorge – In Voll- und Teilzeit 5 Locations posted: Posted Yesterday", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.550686455bac0f22b9", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b075d13d549a619b2c2af6f19c030570", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "(Senior) Mathematiker:in / Aktuar:in* – Betriebliche Altersvorsorge – In Voll- und Teilzeit", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:d4c56d1519ae0b2914abbfdc", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.bb425f7c60e42fddff", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "b075d13d549a619b2c2af6f19c030570", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:b075d13d549a619b2c2af6f19c030570:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Dsseldorf---Peter-Mller/XMLNAME--Senior--Mathematiker-in---Aktuar-in----Betriebliche-Altersvorsorge---In-Voll--und-Teilzeit_R_338415", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "f993529360e74a9e7931227fdbbc970d", "title": "Senior Consultant", "employer_name": "Neural Magic", "employer_slug": "neural-magic", "location_text": "Canberra", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "a16z" ], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://redhat.wd5.myworkdayjobs.com/jobs/job/Canberra/Senior-Consultant_R-040324-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Consultant Canberra posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.ab373205a46aaa1c6c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f993529360e74a9e7931227fdbbc970d", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "Senior Consultant", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:07825c842c4bef17f7da4f4a", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.4d91f9dc68e2374966", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f993529360e74a9e7931227fdbbc970d", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f993529360e74a9e7931227fdbbc970d:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://redhat.wd5.myworkdayjobs.com/jobs/job/Canberra/Senior-Consultant_R-040324-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://redhat.wd5.myworkdayjobs.com/jobs/job/Canberra/Senior-Consultant_R-040324-1", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "a6d9cba0714096d91583d3b88c74cbad", "title": "Senior Machine Learning Engineer", "employer_name": "Equifax", "employer_slug": "equifax", "location_text": "IRL - County Wexford - Wexford", "country": "IE", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-10T00:00:00.000Z", "apply_url": "https://equifax.wd5.myworkdayjobs.com/External/job/IRL---County-Wexford---Wexford/Senior-Machine-Learning-Engineer_J00176887-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer IRL - County Wexford - Wexford posted: Posted 2 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.5ba0ae005f1c4439e4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a6d9cba0714096d91583d3b88c74cbad", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "Senior Machine Learning Engineer", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:67ac818d92f47714a6fb52b0", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.db57c9d18438e0a152", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a6d9cba0714096d91583d3b88c74cbad", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:a6d9cba0714096d91583d3b88c74cbad:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://equifax.wd5.myworkdayjobs.com/External/job/IRL---County-Wexford---Wexford/Senior-Machine-Learning-Engineer_J00176887-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://equifax.wd5.myworkdayjobs.com/External/job/IRL---County-Wexford---Wexford/Senior-Machine-Learning-Engineer_J00176887-1", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "f6af69517f70c9bb82cf1c05ae8ee6d8", "title": "Senior Principal Statistical Programmer", "employer_name": "Novartis", "employer_slug": "novartis", "location_text": "East Hanover", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://novartis.wd3.myworkdayjobs.com/Internal_Careers_for_Acquired_Entities/job/East-Hanover/Senior-Principal-Statistical-Programmer_REQ-10069343", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Principal Statistical Programmer East Hanover posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "staff_plus", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 8, "years_experience_max": 12, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.a9ca733e4a50fd3244", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f6af69517f70c9bb82cf1c05ae8ee6d8", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "Senior Principal Statistical Programmer", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:9ab324cb9253cfff9440b364", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.e31a85a875b3aea58b", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f6af69517f70c9bb82cf1c05ae8ee6d8", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f6af69517f70c9bb82cf1c05ae8ee6d8:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://novartis.wd3.myworkdayjobs.com/Internal_Careers_for_Acquired_Entities/job/East-Hanover/Senior-Principal-Statistical-Programmer_REQ-10069343", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null } } }, { "id": "8662b5b706f6b771690d637c90e9b117", "title": "Senior Estimator", "employer_name": "Bouygues", "employer_slug": "bouygues", "location_text": "Location not specified", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://bouygues.jazz.co/apply/Y8eWubUbv5/Senior-Estimator", "apply_url_verified": false, "ats": "jazzhr", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Estimator Senior Estimator", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.6e622cbd0d5b08d908", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8662b5b706f6b771690d637c90e9b117", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "Senior Estimator", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:d490c0d1900f4a7392394e9a", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.880060e67a7ea2f5c1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8662b5b706f6b771690d637c90e9b117", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8662b5b706f6b771690d637c90e9b117:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://bouygues.jazz.co/apply/Y8eWubUbv5/Senior-Estimator", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null } } }, { "id": "0c124c60fb2a4f31f6f2fa5a6e5d1a4b", "title": "Intern Synthetic Data Generation for Autonomous Driving (f/m/x)", "employer_name": "BMW", "employer_slug": "bmw", "location_text": "Location not specified", "country": "unknown", "employment_type": "internship", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-12T10:25:24.000Z", "apply_url": "https://www.bmwgroup.jobs/job-invite/180842/?locale=en_US", "apply_url_verified": false, "ats": "successfactors", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Intern Synthetic Data Generation for Autonomous Driving (f/m/x) Location not specified SOME IT WORKS. SOME CHANGES WHAT'S POSSIBLE. SHARE YOUR PASSION. More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. And that's why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table - and give you the opportunity to really show what you can do. We focus on producing high-quality synthetic simulation data using advanced generative AI models to effectively train and validate machine learning algorithms within autonomous driving environments. What awaits you? You will support the advancement of synthetic data generation for autonomous driving. Furthermore, you help to develop and optimize advanced data generation and simulation models. In addition, you will assist with hands-on tasks using cutting-edge computer vision and latent diffusion models. Moreover, you contribute to impactful solutions for autonomous driving environments. Furthermore, you support the optimization of machine learning pipelines, architectures, and algorithms. What should you bring along? Studies in computer science, artificial intelligence, or a related field. Advanced expertise in machine learning and deep learning, both theoretical and practical. Proficiency in Python, PyTorch, and Huggingface. Hands-on experience with generative models, especially diffusion-based architectures. Skill in designing and optimizing", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "contractor_type": { "field": "contractor_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "contractor_type" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "entry", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 0, "years_experience_max": 4, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.8966ce728bb6e35d71", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0c124c60fb2a4f31f6f2fa5a6e5d1a4b", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Intern Synthetic Data Generation for Autonomous Driving (f/m/x) Location not specified SOME IT WORKS. SOME CHANGES WHAT'S POSSIBLE. SHARE YOUR PASSION. More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. And that's why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table - and give you the opportunity to really show what you can do. We focus on producing high-quality synthetic simulation data using advanced generative AI models to effectively train and validate machine learning algorithms within autonomous driving environments. What awaits you? You will support the advancement of synthetic data generation for autonomous driving. Furthermore, you help to develop and optimize advanced data generation and simulation models. In addition, you will assist with hands-on tasks using cutting-edge computer vision and latent diffusion models. Moreover, you contribute to impactful solutions for autonomous driving environments. Furthermore, you support the optimization of machine learning pipelines, architectures, and algorithms. What should you bring along? Studies in computer science, artificial intelligence, or a related field. Advanced expertise in machine learning and deep learning, both theoretical and practical. Proficiency in Python, PyTorch, and Huggingface. Hands-on experience with generative models, especially diffusion-based architectures. Skill in designing and optimizing", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:0c124c60fb2a4f31f6f2fa5a6e5d1a4b:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.description_excerpt.f26887d274e3e422e3", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0c124c60fb2a4f31f6f2fa5a6e5d1a4b", "signal_type": "query_match", "display_text": "Description: semantic match", "tooltip": "Description provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "description_excerpt", "source_value": "Intern Synthetic Data Generation for Autonomous Driving (f/m/x) Location not specified SOME IT WORKS. SOME CHANGES WHAT'S POSSIBLE. SHARE YOUR PASSION. More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. And that's why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table - and give you the opportunity to really show what you can do. We focus on producing high-quality synthetic simulation data using advanced generative AI models to effectively train and validate machine learning algorithms within autonomous driving environments. What awaits you? You will support the advancement of synthetic data generation for autonomous driving. Furthermore, you help to develop and optimize advanced data generation and simulation models. In addition, you will assist with hands-on tasks using cutting-edge computer vision and latent diffusion models. Moreover, you contribute to impactful solutions for autonomous driving environments. Furthermore, you support the optimization of machine learning pipelines, architectures, and algorithms. What should you bring along? Studies in computer science, artificial intelligence, or a related field. Advanced expertise in machine learning and deep learning, both theoretical and practical. Proficiency in Python, PyTorch, and Huggingface. Hands-on experience with generative models, especially diffusion-based architectures. Skill in designing and optimizing", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:0d506869b939f6a7efe85397", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.9a8ced2bb4565ddc16", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0c124c60fb2a4f31f6f2fa5a6e5d1a4b", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Intern Synthetic Data Generation for Autonomous Driving (f/m/x) Location not specified SOME IT WORKS. SOME CHANGES WHAT'S POSSIBLE. SHARE YOUR PASSION. More than 90% of automotive innovations are based on electronics and software. That's why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. And that's why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table - and give you the opportunity to really show what you can do. We focus on producing high-quality synthetic simulation data using advanced generative AI models to effectively train and validate machine learning algorithms within autonomous driving environments. What awaits you? You will support the advancement of synthetic data generation for autonomous driving. Furthermore, you help to develop and optimize advanced data generation and simulation models. In addition, you will assist with hands-on tasks using cutting-edge computer vision and latent diffusion models. Moreover, you contribute to impactful solutions for autonomous driving environments. Furthermore, you support the optimization of machine learning pipelines, architectures, and algorithms. What should you bring along? Studies in computer science, artificial intelligence, or a related field. Advanced expertise in machine learning and deep learning, both theoretical and practical. Proficiency in Python, PyTorch, and Huggingface. Hands-on experience with generative models, especially diffusion-based architectures. Skill in designing and optimizing", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.bmwgroup.jobs/job-invite/180842/?locale=en_US", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.bmwgroup.jobs/job-invite/180842/?locale=en_US", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "7dea8af3335a04c862c611ca3352697b", "title": "Lead UI Engineer", "employer_name": "2K", "employer_slug": "2k", "location_text": "San Mateo, California, United States", "country": "US", "employment_type": "full_time", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 133900, "salary_max": 198160, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "total_comp", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 133900, "base_salary_max": 198160, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "espp" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": true, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-03-24T17:06:57.000Z", "apply_url": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Lead UI Engineer San Mateo, California, United States WE ARE GAME MAKERS Who We Are: We are a diverse team of developers driven by a passion for our art, united by our core values and inspired by a culture of inclusivity to build amazing games that thrill players everywhere. We pursue growth and innovation in an environment of safety and trust. Our culture is built on the belief that the more varied voices in our collective will strengthen our team and our games. We are looking for our next teammate who will raise our bar and make us better. What We Need: We're seeking a Lead UI Engineer, to provide tech direction for our AAA multiplayer project and leadership for our team of UI engineers. In this role, you will design and build flexible, scalable, accessible, and high-performance UI systems using modern UE5 frameworks (CommonUI, MVVM, Enhanced Input), while partnering closely with engineers and cross-disciplinary teams. The ideal candidate has deep experience in all aspects of UI development - including hitch-free resource management, integration with latent backend services, support for a wide set of display devices and high frame rates, console-specific requirements, and live configuration. What You'll Do: - Engage creatively with Design and Production on new features to identify opportunities, challenges and cost. - Direct the technical design and implementation of the game's UI using highly supported / best-in-class patterns and UE frameworks. - Maintain robust solutions for asynchronous asset management and integration with latent backend services (e.g. inventory,", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "equity_type": { "field": "equity_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_type" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 7, "years_experience_max": 9, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.8b7b5b98618f839f2c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "7dea8af3335a04c862c611ca3352697b", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Lead UI Engineer San Mateo, California, United States WE ARE GAME MAKERS Who We Are: We are a diverse team of developers driven by a passion for our art, united by our core values and inspired by a culture of inclusivity to build amazing games that thrill players everywhere. We pursue growth and innovation in an environment of safety and trust. Our culture is built on the belief that the more varied voices in our collective will strengthen our team and our games. We are looking for our next teammate who will raise our bar and make us better. What We Need: We're seeking a Lead UI Engineer, to provide tech direction for our AAA multiplayer project and leadership for our team of UI engineers. In this role, you will design and build flexible, scalable, accessible, and high-performance UI systems using modern UE5 frameworks (CommonUI, MVVM, Enhanced Input), while partnering closely with engineers and cross-disciplinary teams. The ideal candidate has deep experience in all aspects of UI development - including hitch-free resource management, integration with latent backend services, support for a wide set of display devices and high frame rates, console-specific requirements, and live configuration. What You'll Do: - Engage creatively with Design and Production on new features to identify opportunities, challenges and cost. - Direct the technical design and implementation of the game's UI using highly supported / best-in-class patterns and UE frameworks. - Maintain robust solutions for asynchronous asset management and integration with latent backend services (e.g. inventory,", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:7dea8af3335a04c862c611ca3352697b:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.description_excerpt.42474e4202040d0154", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "7dea8af3335a04c862c611ca3352697b", "signal_type": "query_match", "display_text": "Description: semantic match", "tooltip": "Description provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "description_excerpt", "source_value": "Lead UI Engineer San Mateo, California, United States WE ARE GAME MAKERS Who We Are: We are a diverse team of developers driven by a passion for our art, united by our core values and inspired by a culture of inclusivity to build amazing games that thrill players everywhere. We pursue growth and innovation in an environment of safety and trust. Our culture is built on the belief that the more varied voices in our collective will strengthen our team and our games. We are looking for our next teammate who will raise our bar and make us better. What We Need: We're seeking a Lead UI Engineer, to provide tech direction for our AAA multiplayer project and leadership for our team of UI engineers. In this role, you will design and build flexible, scalable, accessible, and high-performance UI systems using modern UE5 frameworks (CommonUI, MVVM, Enhanced Input), while partnering closely with engineers and cross-disciplinary teams. The ideal candidate has deep experience in all aspects of UI development - including hitch-free resource management, integration with latent backend services, support for a wide set of display devices and high frame rates, console-specific requirements, and live configuration. What You'll Do: - Engage creatively with Design and Production on new features to identify opportunities, challenges and cost. - Direct the technical design and implementation of the game's UI using highly supported / best-in-class patterns and UE frameworks. - Maintain robust solutions for asynchronous asset management and integration with latent backend services (e.g. inventory,", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:b980197ff2de26f5cbff459c", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.5105ffeaefd22fd0fd", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "7dea8af3335a04c862c611ca3352697b", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Lead UI Engineer San Mateo, California, United States WE ARE GAME MAKERS Who We Are: We are a diverse team of developers driven by a passion for our art, united by our core values and inspired by a culture of inclusivity to build amazing games that thrill players everywhere. We pursue growth and innovation in an environment of safety and trust. Our culture is built on the belief that the more varied voices in our collective will strengthen our team and our games. We are looking for our next teammate who will raise our bar and make us better. What We Need: We're seeking a Lead UI Engineer, to provide tech direction for our AAA multiplayer project and leadership for our team of UI engineers. In this role, you will design and build flexible, scalable, accessible, and high-performance UI systems using modern UE5 frameworks (CommonUI, MVVM, Enhanced Input), while partnering closely with engineers and cross-disciplinary teams. The ideal candidate has deep experience in all aspects of UI development - including hitch-free resource management, integration with latent backend services, support for a wide set of display devices and high frame rates, console-specific requirements, and live configuration. What You'll Do: - Engage creatively with Design and Production on new features to identify opportunities, challenges and cost. - Direct the technical design and implementation of the game's UI using highly supported / best-in-class patterns and UE frameworks. - Maintain robust solutions for asynchronous asset management and integration with latent backend services (e.g. inventory,", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/2k/jobs/7677521003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "5a27c6e84dd83c1e44af91c31108b4b5", "title": "Senior statistical Programmer", "employer_name": "IQVIA", "employer_slug": "iqvia", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "profit_share" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://iqvia.wd1.myworkdayjobs.com/IQVIA/job/Taipei--Taipei-City-Taiwan/Senior-statistical-Programmer_R1543520", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior statistical Programmer 2 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Healthcare", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.title.f638e61f569a3d9a07", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "5a27c6e84dd83c1e44af91c31108b4b5", "signal_type": "query_match", "display_text": "Title: semantic match", "tooltip": "Title provided semantic evidence for your search intent.", "source_binding": "reranker_semantic_evidence", "source_field": "title", "source_value": "Senior statistical Programmer", "matched_input": "latent", "derivation_source": "rerank_result.semantic_evidence", "candidate_evidence_id": null, "rerank_evidence_id": "rerank:ccc372d7c774d4853f476ea1", "taxonomy_alias_id": null, "oracle_verification_method": "reranker_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 86, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.9b2f9510035acef877", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "5a27c6e84dd83c1e44af91c31108b4b5", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:5a27c6e84dd83c1e44af91c31108b4b5:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://iqvia.wd1.myworkdayjobs.com/IQVIA/job/Taipei--Taipei-City-Taiwan/Senior-statistical-Programmer_R1543520", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "c8ac7d3436f8e019b671b3038acf9773", "title": "Senior Machine Learning Engineer", "employer_name": "Expedia Group", "employer_slug": "expedia-group", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T00:00:00.000Z", "apply_url": "https://expedia.wd108.myworkdayjobs.com/search/job/USA---California---San-Jose/Senior-Machine-Learning-Engineer_R-105618-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer 2 Locations posted: Posted Yesterday", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1324424/000132442426000008/expe-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1324424/000132442426000008/expe-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.902129cd12713d0feb", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "c8ac7d3436f8e019b671b3038acf9773", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:c8ac7d3436f8e019b671b3038acf9773:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1324424/000132442426000008/expe-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1324424/000132442426000008/expe-20251231.htm" ], "checked_at": null }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://expedia.wd108.myworkdayjobs.com/search/job/USA---California---San-Jose/Senior-Machine-Learning-Engineer_R-105618-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://expedia.wd108.myworkdayjobs.com/search/job/USA---California---San-Jose/Senior-Machine-Learning-Engineer_R-105618-1", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "abdc71a33112ca65abba12981e7cf015", "title": "Senior Researcher Psychometric Assessments", "employer_name": "Marsh McLennan", "employer_slug": "marsh-mclennan", "location_text": "Gurugram - Good Earth", "country": "IN", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Gurugram---Good-Earth/Senior-Researcher-Psychometric-Assessments_R_316444", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Researcher Psychometric Assessments Gurugram - Good Earth posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.20309d911b2fc0c3bf", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "abdc71a33112ca65abba12981e7cf015", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:abdc71a33112ca65abba12981e7cf015:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Gurugram---Good-Earth/Senior-Researcher-Psychometric-Assessments_R_316444", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "3554c65a748c82e62d5f5015bfdc9dfa", "title": "Research Scientist - Audio Codec", "employer_name": "Mirelo AI", "employer_slug": "mirelo-ai", "location_text": "Berlin, Germany, Tübingen, Hybrid", "country": "DE", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "a16z" ], "posted_at": "2025-12-03T19:49:21.458Z", "apply_url": "https://jobs.ashbyhq.com/mirelo/55090c78-9dee-4fa3-bf29-c06ad65c0f02", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Research Scientist - Audio Codec Berlin, Germany, Tübingen, Hybrid Mirelo AI is building the next generation of creative tools by generating realistic sound, speech and music from video. We develop cutting-edge foundational generative AI models that \"unmute\" silent video content and create custom, hyper-realistic audio for gaming, video platforms, and creators. Our technology empowers global storytellers to transform their content. We recently closed a $41 million Seed round co-led by Andreessen Horowitz and Index Ventures with participation from Atlantic, and are rapidly expanding across Product, Engineering, Go-to-Market, and Growth. About the Role At Mirelo, we're pushing the limits of what generative audio can do, and our ability to innovate depends heavily on the quality of our underlying audio representations. In this role, you'll work at the core of our modeling stack - designing, training, and evaluating neural audio codecs that directly shape the performance of our next-generation music and sound models. You'll collaborate closely with the model team, experiment with both continuous and discrete representations, and build the evaluation tools that help us understand what actually moves the needle. Your work will sit at the foundation of building the best-sounding generative models in the world. Key Responsibilities - Develop and implement new neural audio codecs for sound, music and speech that push the state-of-the art in sound quality and are optimized for the the use case of generative models. - Think about the specific challenges that arise when the codec is primarily used as a latent representation in the context", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "seed", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.35b804b1ee4b454b2f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "3554c65a748c82e62d5f5015bfdc9dfa", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Research Scientist - Audio Codec Berlin, Germany, Tübingen, Hybrid Mirelo AI is building the next generation of creative tools by generating realistic sound, speech and music from video. We develop cutting-edge foundational generative AI models that \"unmute\" silent video content and create custom, hyper-realistic audio for gaming, video platforms, and creators. Our technology empowers global storytellers to transform their content. We recently closed a $41 million Seed round co-led by Andreessen Horowitz and Index Ventures with participation from Atlantic, and are rapidly expanding across Product, Engineering, Go-to-Market, and Growth. About the Role At Mirelo, we're pushing the limits of what generative audio can do, and our ability to innovate depends heavily on the quality of our underlying audio representations. In this role, you'll work at the core of our modeling stack - designing, training, and evaluating neural audio codecs that directly shape the performance of our next-generation music and sound models. You'll collaborate closely with the model team, experiment with both continuous and discrete representations, and build the evaluation tools that help us understand what actually moves the needle. Your work will sit at the foundation of building the best-sounding generative models in the world. Key Responsibilities - Develop and implement new neural audio codecs for sound, music and speech that push the state-of-the art in sound quality and are optimized for the the use case of generative models. - Think about the specific challenges that arise when the codec is primarily used as a latent representation in the context", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:3554c65a748c82e62d5f5015bfdc9dfa:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.63e075fa0bed39d26f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "3554c65a748c82e62d5f5015bfdc9dfa", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Research Scientist - Audio Codec Berlin, Germany, Tübingen, Hybrid Mirelo AI is building the next generation of creative tools by generating realistic sound, speech and music from video. We develop cutting-edge foundational generative AI models that \"unmute\" silent video content and create custom, hyper-realistic audio for gaming, video platforms, and creators. Our technology empowers global storytellers to transform their content. We recently closed a $41 million Seed round co-led by Andreessen Horowitz and Index Ventures with participation from Atlantic, and are rapidly expanding across Product, Engineering, Go-to-Market, and Growth. About the Role At Mirelo, we're pushing the limits of what generative audio can do, and our ability to innovate depends heavily on the quality of our underlying audio representations. In this role, you'll work at the core of our modeling stack - designing, training, and evaluating neural audio codecs that directly shape the performance of our next-generation music and sound models. You'll collaborate closely with the model team, experiment with both continuous and discrete representations, and build the evaluation tools that help us understand what actually moves the needle. Your work will sit at the foundation of building the best-sounding generative models in the world. Key Responsibilities - Develop and implement new neural audio codecs for sound, music and speech that push the state-of-the art in sound quality and are optimized for the the use case of generative models. - Think about the specific challenges that arise when the codec is primarily used as a latent representation in the context", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/mirelo/55090c78-9dee-4fa3-bf29-c06ad65c0f02", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/mirelo/55090c78-9dee-4fa3-bf29-c06ad65c0f02", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/mirelo/55090c78-9dee-4fa3-bf29-c06ad65c0f02", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/mirelo/55090c78-9dee-4fa3-bf29-c06ad65c0f02", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/mirelo/55090c78-9dee-4fa3-bf29-c06ad65c0f02", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "7a5a4a1c7fcf7545036388f7148357ce", "title": "Senior Machine Learning Engineer", "employer_name": "Cognex Corporation", "employer_slug": "cognex", "location_text": "Natick, Massachusetts", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://cognex.wd1.myworkdayjobs.com/External_Career_Site/job/Natick-Massachusetts/Senior-Machine-Learning-Engineer_R0011776", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer Natick, Massachusetts posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/851205/000085120526000012/cgnx-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/851205/000085120526000012/cgnx-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.6eb7ffa7358dd33fe9", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "7a5a4a1c7fcf7545036388f7148357ce", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:7a5a4a1c7fcf7545036388f7148357ce:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/851205/000085120526000012/cgnx-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/851205/000085120526000012/cgnx-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://cognex.wd1.myworkdayjobs.com/External_Career_Site/job/Natick-Massachusetts/Senior-Machine-Learning-Engineer_R0011776", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://cognex.wd1.myworkdayjobs.com/External_Career_Site/job/Natick-Massachusetts/Senior-Machine-Learning-Engineer_R0011776", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "4419ac05b680ab2318d03fa688ea0efe", "title": "Senior Partnerships Manager", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "Freiburg (Germany), San Francisco (USA)", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": 175000, "salary_max": 240000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 175000, "base_salary_max": 240000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2025-12-01T12:32:10.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5014558008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Partnerships Manager Freiburg (Germany), San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role This isn't a \"close deals and hit quotas\" role. You'll be architecting the commercial layer of a technology that's still being invented. What does pricing look like when your product gets exponentially better every quarter? How do you structure partnerships when your API serves both scrappy startups and global enterprises? What distribution channels don't exist yet-but should? You'll work directly with our technical team to translate research breakthroughs into market opportunities, often before the market knows it needs them. One week you might be negotiating custom enterprise agreements; the next, designing pricing models for capabilities we just shipped. What You'll Work On - Drive business growth through strategic partnerships and custom deal development, from initial outreach through close - Develop pricing strategies for capabilities that may not have existed six months ago - Collaborate with Research, Engineering, Finance, and Legal to lead complex product,", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "sales", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.77adf7698636e7d3fd", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4419ac05b680ab2318d03fa688ea0efe", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior Partnerships Manager Freiburg (Germany), San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role This isn't a \"close deals and hit quotas\" role. You'll be architecting the commercial layer of a technology that's still being invented. What does pricing look like when your product gets exponentially better every quarter? How do you structure partnerships when your API serves both scrappy startups and global enterprises? What distribution channels don't exist yet-but should? You'll work directly with our technical team to translate research breakthroughs into market opportunities, often before the market knows it needs them. One week you might be negotiating custom enterprise agreements; the next, designing pricing models for capabilities we just shipped. What You'll Work On - Drive business growth through strategic partnerships and custom deal development, from initial outreach through close - Develop pricing strategies for capabilities that may not have existed six months ago - Collaborate with Research, Engineering, Finance, and Legal to lead complex product,", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:4419ac05b680ab2318d03fa688ea0efe:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.995cb35cd0cc54d66d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4419ac05b680ab2318d03fa688ea0efe", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Senior Partnerships Manager Freiburg (Germany), San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role This isn't a \"close deals and hit quotas\" role. You'll be architecting the commercial layer of a technology that's still being invented. What does pricing look like when your product gets exponentially better every quarter? How do you structure partnerships when your API serves both scrappy startups and global enterprises? What distribution channels don't exist yet-but should? You'll work directly with our technical team to translate research breakthroughs into market opportunities, often before the market knows it needs them. One week you might be negotiating custom enterprise agreements; the next, designing pricing models for capabilities we just shipped. What You'll Work On - Drive business growth through strategic partnerships and custom deal development, from initial outreach through close - Develop pricing strategies for capabilities that may not have existed six months ago - Collaborate with Research, Engineering, Finance, and Legal to lead complex product,", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5014558008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5014558008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5014558008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5014558008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5014558008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5014558008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "78fc7e3d7bf24f490215b6158d58a8a8", "title": "Software Development Engineer (Prime Video), Live Events Publishing", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Bengaluru, Karnataka, IND", "country": "IN", "employment_type": "internship", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-10T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Development Engineer (Prime Video), Live Events Publishing Bengaluru, Karnataka, IND Want to change the way people watch Live Sports? Passionate about building, owning and operating massively scalable systems? Looking forward to work with a team of highly competent software engineers ranging from new graduates to seasoned architects building software using the latest technologies? Are you looking for an inclusive team, where you feel safe to be yourself, to share your ideas openly, to experiment, fail and learn, and where you help make decisions that shape the team's path forward? If so, here is an opportunity tailored for you. The Prime Video Sports Publishing (LEP) team is the owner, steward, and authority responsible for the sports catalog that powers the live sports experience for Prime Video customers worldwide. As an SDE-2 on the LEP team, you will play a pivotal role in building and operating the suite of services that fulfill our vision to be the world's richest, reliable and low latent catalog that enables sports fans worldwide to discover, purchase and consume sports content in any format, on any device. If you are passionate about transforming the live sports streaming experience and have the technical expertise to build innovative solutions at scale, we'd love to have you join our team. Key job responsibilities * Design, develop and maintain core catalog services that ingest, enrich, and publish data on live sports events, broadcasts, and associated content * Build distributed systems to manage high-volume, low-latency publishing workflows that ensure events are", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 52, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.47e72dcfa37a8e66f1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "78fc7e3d7bf24f490215b6158d58a8a8", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Development Engineer (Prime Video), Live Events Publishing Bengaluru, Karnataka, IND Want to change the way people watch Live Sports? Passionate about building, owning and operating massively scalable systems? Looking forward to work with a team of highly competent software engineers ranging from new graduates to seasoned architects building software using the latest technologies? Are you looking for an inclusive team, where you feel safe to be yourself, to share your ideas openly, to experiment, fail and learn, and where you help make decisions that shape the team's path forward? If so, here is an opportunity tailored for you. The Prime Video Sports Publishing (LEP) team is the owner, steward, and authority responsible for the sports catalog that powers the live sports experience for Prime Video customers worldwide. As an SDE-2 on the LEP team, you will play a pivotal role in building and operating the suite of services that fulfill our vision to be the world's richest, reliable and low latent catalog that enables sports fans worldwide to discover, purchase and consume sports content in any format, on any device. If you are passionate about transforming the live sports streaming experience and have the technical expertise to build innovative solutions at scale, we'd love to have you join our team. Key job responsibilities * Design, develop and maintain core catalog services that ingest, enrich, and publish data on live sports events, broadcasts, and associated content * Build distributed systems to manage high-volume, low-latency publishing workflows that ensure events are", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:78fc7e3d7bf24f490215b6158d58a8a8:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.120c63bf1ba038d103", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "78fc7e3d7bf24f490215b6158d58a8a8", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Development Engineer (Prime Video), Live Events Publishing Bengaluru, Karnataka, IND Want to change the way people watch Live Sports? Passionate about building, owning and operating massively scalable systems? Looking forward to work with a team of highly competent software engineers ranging from new graduates to seasoned architects building software using the latest technologies? Are you looking for an inclusive team, where you feel safe to be yourself, to share your ideas openly, to experiment, fail and learn, and where you help make decisions that shape the team's path forward? If so, here is an opportunity tailored for you. The Prime Video Sports Publishing (LEP) team is the owner, steward, and authority responsible for the sports catalog that powers the live sports experience for Prime Video customers worldwide. As an SDE-2 on the LEP team, you will play a pivotal role in building and operating the suite of services that fulfill our vision to be the world's richest, reliable and low latent catalog that enables sports fans worldwide to discover, purchase and consume sports content in any format, on any device. If you are passionate about transforming the live sports streaming experience and have the technical expertise to build innovative solutions at scale, we'd love to have you join our team. Key job responsibilities * Design, develop and maintain core catalog services that ingest, enrich, and publish data on live sports events, broadcasts, and associated content * Build distributed systems to manage high-volume, low-latency publishing workflows that ensure events are", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "fertility_family_building_benefits": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "adoption_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "childcare_subsidy": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444626/software-development-engineer-prime-video-live-events-publishing", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "aca1fc7ee5c75a11c3e9269f94f2d716", "title": "Senior Quantitative Researcher", "employer_name": "WorldQuant", "employer_slug": "worldquant", "location_text": "Yerevan", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": true, "top_startup_sources": [], "posted_at": "2023-04-20T06:58:09.000Z", "apply_url": "https://job-boards.greenhouse.io/worldquant/jobs/4069493006", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Quantitative Researcher Yerevan WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies - the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. Job Responsibilities (including, but not limited to, the following): - Building algorithmic, computer-driven models; - Conducting research on academic quantitative finance literature; - Exploring new data; - Applying an alignment of innovative methods in Applied Mathematics, Computer Science and Financial Economics. What You'll Bring: - Proven 3 years experience in quantitative equity research (buy side or sell side); - Background in working with large data sets; - Degree from a top university in a highly analytical/quantitative field, such as: Mathematics, Finance or Economics, Computer Science, Physics, Engineering or similar; - Research mind-set: be a problem solver, creative, steadfast, smart,", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": true, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" }, "relocation_assistance": { "field": "relocation_assistance", "source": "llm:greenfield:inv_371_postfix", "db_column": "relocation_assistance" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.413341a5012bab7abc", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "aca1fc7ee5c75a11c3e9269f94f2d716", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:aca1fc7ee5c75a11c3e9269f94f2d716:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4069493006", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4069493006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4069493006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4069493006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "relocation_assistance": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4069493006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "8b916600c629d7f11a4810120a39eb3f", "title": "Senior Machine Learning Engineer", "employer_name": "Workday, Inc.", "employer_slug": "workday", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://workday.wd5.myworkdayjobs.com/Workday/job/Canada-ON-Toronto/Senior-Machine-Learning-Engineer_JR-0106107", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer 2 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": 12, "non_birth_parent_leave_weeks": 12, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://workdaybenefits.com/us/social-and-flex/leaves-of-absence", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 89, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1327811/000132781126000014/wday-20260131.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1327811/000132781126000014/wday-20260131.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://workdaybenefits.com/us/social-and-flex/leaves-of-absence", "db_column": "parental_leave_weeks", "source_url": "https://workdaybenefits.com/us/social-and-flex/leaves-of-absence" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://workdaybenefits.com/us/social-and-flex/leaves-of-absence", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://workdaybenefits.com/us/social-and-flex/leaves-of-absence" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Technology", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.30a4ac16fb7437b58a", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8b916600c629d7f11a4810120a39eb3f", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8b916600c629d7f11a4810120a39eb3f:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1327811/000132781126000014/wday-20260131.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1327811/000132781126000014/wday-20260131.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://workday.wd5.myworkdayjobs.com/Workday/job/Canada-ON-Toronto/Senior-Machine-Learning-Engineer_JR-0106107", "source_values": [ "https://workdaybenefits.com/us/social-and-flex/leaves-of-absence" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://workday.wd5.myworkdayjobs.com/Workday/job/Canada-ON-Toronto/Senior-Machine-Learning-Engineer_JR-0106107", "source_values": [ "https://workdaybenefits.com/us/social-and-flex/leaves-of-absence" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://workday.wd5.myworkdayjobs.com/Workday/job/Canada-ON-Toronto/Senior-Machine-Learning-Engineer_JR-0106107", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://workday.wd5.myworkdayjobs.com/Workday/job/Canada-ON-Toronto/Senior-Machine-Learning-Engineer_JR-0106107", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "8c8f5409730a9324b3de0b7331c4688a", "title": "Developer Experience Engineer", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "Freiburg (Germany)", "country": "DE", "employment_type": "contract", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": 140000, "salary_max": 190000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "EUR", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 140000, "base_salary_max": 190000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2026-05-27T17:28:30.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Developer Experience Engineer Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role FLUX is the model developers want. BFL still has to become the platform they choose. In 2026 that platform is judged by a single question, asked by both humans and the agents acting on their behalf: how fast can I go from idea to a production-shaped call, and how good does it feel along the way? We're hiring one person to own that answer end-to-end. Not the SDK alone, not the dashboard alone, not the API alone - the whole developer experience, for both the humans building on BFL and the agents building on their behalf. You're the person on the team who breathes DevExp: spotting where the platform is friction for a developer or for an agent, deciding whether the fix lives in the API contract, the SDK, the console, the docs surface, the MCP server, or the onboarding flow - and then shipping it. You", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 4, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.d809308637174aacf2", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8c8f5409730a9324b3de0b7331c4688a", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Developer Experience Engineer Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role FLUX is the model developers want. BFL still has to become the platform they choose. In 2026 that platform is judged by a single question, asked by both humans and the agents acting on their behalf: how fast can I go from idea to a production-shaped call, and how good does it feel along the way? We're hiring one person to own that answer end-to-end. Not the SDK alone, not the dashboard alone, not the API alone - the whole developer experience, for both the humans building on BFL and the agents building on their behalf. You're the person on the team who breathes DevExp: spotting where the platform is friction for a developer or for an agent, deciding whether the fix lives in the API contract, the SDK, the console, the docs surface, the MCP server, or the onboarding flow - and then shipping it. You", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8c8f5409730a9324b3de0b7331c4688a:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.706d5c2c415fcac551", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8c8f5409730a9324b3de0b7331c4688a", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Developer Experience Engineer Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role FLUX is the model developers want. BFL still has to become the platform they choose. In 2026 that platform is judged by a single question, asked by both humans and the agents acting on their behalf: how fast can I go from idea to a production-shaped call, and how good does it feel along the way? We're hiring one person to own that answer end-to-end. Not the SDK alone, not the dashboard alone, not the API alone - the whole developer experience, for both the humans building on BFL and the agents building on their behalf. You're the person on the team who breathes DevExp: spotting where the platform is friction for a developer or for an agent, deciding whether the fix lives in the API contract, the SDK, the console, the docs surface, the MCP server, or the onboarding flow - and then shipping it. You", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5231540008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "28058ce695bc780e60e12d820b158dae", "title": "Agentic AI & Data Scientist", "employer_name": "Kyndryl", "employer_slug": "kyndryl", "location_text": "Valbonne (KFR51693)", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://kyndryl.wd5.myworkdayjobs.com/KyndrylProfessionalCareers/job/Valbonne-KFR51693/Agentic-AI---Data-Scientist_R-52929", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Agentic AI & Data Scientist Valbonne (KFR51693) posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.8d483f74696df9e9bd", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "28058ce695bc780e60e12d820b158dae", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:28058ce695bc780e60e12d820b158dae:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://kyndryl.wd5.myworkdayjobs.com/KyndrylProfessionalCareers/job/Valbonne-KFR51693/Agentic-AI---Data-Scientist_R-52929", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://kyndryl.wd5.myworkdayjobs.com/KyndrylProfessionalCareers/job/Valbonne-KFR51693/Agentic-AI---Data-Scientist_R-52929", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "a0e18c9a20d7cd121b31b552216e17d0", "title": "People Partner", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "San Francisco", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 145000, "salary_max": 165000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 145000, "base_salary_max": 165000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2026-05-17T20:15:14.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5222243008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "People Partner San Francisco About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role This is a rare opportunity to be the first dedicated People Partner in the region. Two talent colleagues and a workplace manager are already on the ground, but no one yet owns the People agenda. That leaves real scope to build the processes, culture touchpoints, leadership capability, and operational foundation a scaling NA team needs. You'll split your time roughly 60% People Partnering / 40% People Operations, with the balance shifting as the team grows. Reporting to the Head of People (Freiburg), you'll work closely with the broader People team in Europe, acting as the bridge that keeps our US team well-supported in timezone. This role suits someone with strong HRBP experience who can hold a room with senior leaders, but isn't too precious to roll up their sleeves on the ops side when it matters. Deep tech experience is essential; you'll be supporting engineers, researchers, and commercial", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 6, "years_experience_max": 7, "role_function": "hr", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.0c90e4eee141d820cd", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a0e18c9a20d7cd121b31b552216e17d0", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "People Partner San Francisco About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role This is a rare opportunity to be the first dedicated People Partner in the region. Two talent colleagues and a workplace manager are already on the ground, but no one yet owns the People agenda. That leaves real scope to build the processes, culture touchpoints, leadership capability, and operational foundation a scaling NA team needs. You'll split your time roughly 60% People Partnering / 40% People Operations, with the balance shifting as the team grows. Reporting to the Head of People (Freiburg), you'll work closely with the broader People team in Europe, acting as the bridge that keeps our US team well-supported in timezone. This role suits someone with strong HRBP experience who can hold a room with senior leaders, but isn't too precious to roll up their sleeves on the ops side when it matters. Deep tech experience is essential; you'll be supporting engineers, researchers, and commercial", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:a0e18c9a20d7cd121b31b552216e17d0:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.898fcb512e12cc021c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a0e18c9a20d7cd121b31b552216e17d0", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "People Partner San Francisco About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role This is a rare opportunity to be the first dedicated People Partner in the region. Two talent colleagues and a workplace manager are already on the ground, but no one yet owns the People agenda. That leaves real scope to build the processes, culture touchpoints, leadership capability, and operational foundation a scaling NA team needs. You'll split your time roughly 60% People Partnering / 40% People Operations, with the balance shifting as the team grows. Reporting to the Head of People (Freiburg), you'll work closely with the broader People team in Europe, acting as the bridge that keeps our US team well-supported in timezone. This role suits someone with strong HRBP experience who can hold a room with senior leaders, but isn't too precious to roll up their sleeves on the ops side when it matters. Deep tech experience is essential; you'll be supporting engineers, researchers, and commercial", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5222243008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5222243008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5222243008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5222243008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5222243008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5222243008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "6974da6a1c0d9baef672cf47cc769646", "title": "Senior Applied Machine Learning Scientist", "employer_name": "Workiva Inc", "employer_slug": "workiva", "location_text": "USA - Remote", "country": "US", "employment_type": "unknown", "remote_status": "remote", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 0, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-12T00:00:00.000Z", "apply_url": "https://workiva.wd503.myworkdayjobs.com/Careers/job/USA---Remote/Senior-Applied-Machine-Learning-Scientist_R11778", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Applied Machine Learning Scientist USA - Remote posted: Posted Today", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 48, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1445305/000144530526000016/wk-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1445305/000144530526000016/wk-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.cbec1d0311e121e997", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6974da6a1c0d9baef672cf47cc769646", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6974da6a1c0d9baef672cf47cc769646:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://workiva.wd503.myworkdayjobs.com/Careers/job/USA---Remote/Senior-Applied-Machine-Learning-Scientist_R11778", "source_values": [ "rule:remote_status" ], "checked_at": null }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1445305/000144530526000016/wk-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1445305/000144530526000016/wk-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://workiva.wd503.myworkdayjobs.com/Careers/job/USA---Remote/Senior-Applied-Machine-Learning-Scientist_R11778", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://workiva.wd503.myworkdayjobs.com/Careers/job/USA---Remote/Senior-Applied-Machine-Learning-Scientist_R11778", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "a12afcfbfcb678897f0161ea9c998094", "title": "Medical Instrument Technician (Electroencephalography Technician) (EEG)-Open Continuous Announcement", "employer_name": "Department of Veterans Affairs", "employer_slug": "department-of-veterans-affairs", "location_text": "Washington, District of Columbia", "country": "US", "employment_type": "part_time", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 63940, "salary_max": 83119, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 63940, "base_salary_max": 83119, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": 5, "match_401k_pct_source": "llm:greenfield:inv_371_postfix", "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-03-25T00:00:00.000Z", "apply_url": "https://www.usajobs.gov:443/job/862608300", "apply_url_verified": false, "ats": "usajobs", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Medical Instrument Technician (Electroencephalography Technician) (EEG)-Open Continuous Announcement Washington, District of Columbia Summary The Primary Purpose of the Position is to function as an EEG Technologist maintaining state of the art quality in the principles and procedures associated with Electroencephalography. The EEG technician is a member of the neurology department at the Washington DC VA Medical Center where the functions of the job are located. Duties Total Rewards of a Allied Health Professional Electroencephalograph (EEG) Technicians operate the electroencephalograph and other devices such as evoked potential equipment to record the electrical activity of the brain. Registration is offered by the American Board of Registration of EEG, CNIM & EP Technologists (ABRET) or the American Association of Electrodiagnostic Techs (AAET). DUTIES: Provide independent performance of a full range of procedures. Delivers high quality studies for the most complex and non-routine procedures in difficult or demanding environments such as research, intraoperative monitoring, or intensive care monitoring or intensive care monitoring. Interprets study results and brings to the attention of the physician or supervisor more subtle abnormalities that may require intervention. The technician will highlight portions of recordings of clinical importance for review by the clinical neurophysiologist and will accurately describe the concurrent behavior of the patient. Positive interaction with other health care staff to inform them of the indications for testing; the value, possible outcomes, and limitations of testing; and the policies of the laboratory are expected. Obtain multiple sleep latent tests on patients with possible narcolepsy or other disorders of excess", "parental_leave_weeks": 12, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [ "DC" ], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "match_401k_pct": { "field": "match_401k_pct", "source": "llm:greenfield:inv_371_postfix", "db_column": "match_401k_pct" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "hire_states_allowed": { "field": "hire_states_allowed", "source": "rule:state_eligibility", "db_column": "hire_states_allowed" }, "application_deadline": { "field": "application_deadline", "source": "llm:greenfield:inv_371_postfix", "db_column": "application_deadline" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "parental_leave_weeks" }, "us_citizenship_required": { "field": "us_citizenship_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "us_citizenship_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "healthcare", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "Healthcare", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 55, "travel_pct": 25, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": true, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.471e5be1cd7b2a93e2", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a12afcfbfcb678897f0161ea9c998094", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Medical Instrument Technician (Electroencephalography Technician) (EEG)-Open Continuous Announcement Washington, District of Columbia Summary The Primary Purpose of the Position is to function as an EEG Technologist maintaining state of the art quality in the principles and procedures associated with Electroencephalography. The EEG technician is a member of the neurology department at the Washington DC VA Medical Center where the functions of the job are located. Duties Total Rewards of a Allied Health Professional Electroencephalograph (EEG) Technicians operate the electroencephalograph and other devices such as evoked potential equipment to record the electrical activity of the brain. Registration is offered by the American Board of Registration of EEG, CNIM & EP Technologists (ABRET) or the American Association of Electrodiagnostic Techs (AAET). DUTIES: Provide independent performance of a full range of procedures. Delivers high quality studies for the most complex and non-routine procedures in difficult or demanding environments such as research, intraoperative monitoring, or intensive care monitoring or intensive care monitoring. Interprets study results and brings to the attention of the physician or supervisor more subtle abnormalities that may require intervention. The technician will highlight portions of recordings of clinical importance for review by the clinical neurophysiologist and will accurately describe the concurrent behavior of the patient. Positive interaction with other health care staff to inform them of the indications for testing; the value, possible outcomes, and limitations of testing; and the policies of the laboratory are expected. Obtain multiple sleep latent tests on patients with possible narcolepsy or other disorders of excess", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:a12afcfbfcb678897f0161ea9c998094:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.4a774871f184146f42", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a12afcfbfcb678897f0161ea9c998094", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Medical Instrument Technician (Electroencephalography Technician) (EEG)-Open Continuous Announcement Washington, District of Columbia Summary The Primary Purpose of the Position is to function as an EEG Technologist maintaining state of the art quality in the principles and procedures associated with Electroencephalography. The EEG technician is a member of the neurology department at the Washington DC VA Medical Center where the functions of the job are located. Duties Total Rewards of a Allied Health Professional Electroencephalograph (EEG) Technicians operate the electroencephalograph and other devices such as evoked potential equipment to record the electrical activity of the brain. Registration is offered by the American Board of Registration of EEG, CNIM & EP Technologists (ABRET) or the American Association of Electrodiagnostic Techs (AAET). DUTIES: Provide independent performance of a full range of procedures. Delivers high quality studies for the most complex and non-routine procedures in difficult or demanding environments such as research, intraoperative monitoring, or intensive care monitoring or intensive care monitoring. Interprets study results and brings to the attention of the physician or supervisor more subtle abnormalities that may require intervention. The technician will highlight portions of recordings of clinical importance for review by the clinical neurophysiologist and will accurately describe the concurrent behavior of the patient. Positive interaction with other health care staff to inform them of the indications for testing; the value, possible outcomes, and limitations of testing; and the policies of the laboratory are expected. Obtain multiple sleep latent tests on patients with possible narcolepsy or other disorders of excess", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "match_401k_pct": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "hire_states_allowed": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "rule:state_eligibility" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.usajobs.gov:443/job/862608300", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "a46e3a50a385571b3898bf6cd9c51b49", "title": "Principal Applied Scientist, Personalization", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Seattle, Washington, USA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 198900, "salary_max": 269000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 198900, "base_salary_max": 269000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-03T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Principal Applied Scientist, Personalization Seattle, Washington, USA Reinventing How the World Shops! We are building the future of human-AI collaboration in commerce. We are creating an AI-native shopping partner that truly understands what customers mean, what they need, and what they haven't yet realized they want-rivaling the intuition of the best human experts, operating at a scale no human ever could. This is a complex personalization challenge. We sit at the intersection of massive-scale language understanding, real-time decision systems, hundreds of millions of customers, and billions of products. Our mission is to collapse the distance between intent and discovery-to make the leap from \"searching for products\" to \"being understood as a person.\" As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, multidisciplinary organization, and drive breakthroughs that reshape how Amazon thinks about customers and products at the deepest level. The problems you'll solve don't have textbook answers. You will pioneer next-generation LLM-based reasoning systems that build rich, evolving models of customer intent. You will design transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. You will invent real-time, multi-objective ranking systems that balance exploration, personalization, and serendipity at billions of decisions per day. And you will do all of this as a force multiplier, building foundational technology that empowers teams across Amazon to deliver experiences that feel almost magical. Your work will be felt, not just measured. Every", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 56, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "principal", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 12, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.3dee6bfb62b9c125b4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a46e3a50a385571b3898bf6cd9c51b49", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Principal Applied Scientist, Personalization Seattle, Washington, USA Reinventing How the World Shops! We are building the future of human-AI collaboration in commerce. We are creating an AI-native shopping partner that truly understands what customers mean, what they need, and what they haven't yet realized they want-rivaling the intuition of the best human experts, operating at a scale no human ever could. This is a complex personalization challenge. We sit at the intersection of massive-scale language understanding, real-time decision systems, hundreds of millions of customers, and billions of products. Our mission is to collapse the distance between intent and discovery-to make the leap from \"searching for products\" to \"being understood as a person.\" As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, multidisciplinary organization, and drive breakthroughs that reshape how Amazon thinks about customers and products at the deepest level. The problems you'll solve don't have textbook answers. You will pioneer next-generation LLM-based reasoning systems that build rich, evolving models of customer intent. You will design transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. You will invent real-time, multi-objective ranking systems that balance exploration, personalization, and serendipity at billions of decisions per day. And you will do all of this as a force multiplier, building foundational technology that empowers teams across Amazon to deliver experiences that feel almost magical. Your work will be felt, not just measured. Every", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:a46e3a50a385571b3898bf6cd9c51b49:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.b912267f5c6c5a61f8", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a46e3a50a385571b3898bf6cd9c51b49", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Principal Applied Scientist, Personalization Seattle, Washington, USA Reinventing How the World Shops! We are building the future of human-AI collaboration in commerce. We are creating an AI-native shopping partner that truly understands what customers mean, what they need, and what they haven't yet realized they want-rivaling the intuition of the best human experts, operating at a scale no human ever could. This is a complex personalization challenge. We sit at the intersection of massive-scale language understanding, real-time decision systems, hundreds of millions of customers, and billions of products. Our mission is to collapse the distance between intent and discovery-to make the leap from \"searching for products\" to \"being understood as a person.\" As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, multidisciplinary organization, and drive breakthroughs that reshape how Amazon thinks about customers and products at the deepest level. The problems you'll solve don't have textbook answers. You will pioneer next-generation LLM-based reasoning systems that build rich, evolving models of customer intent. You will design transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. You will invent real-time, multi-objective ranking systems that balance exploration, personalization, and serendipity at billions of decisions per day. And you will do all of this as a force multiplier, building foundational technology that empowers teams across Amazon to deliver experiences that feel almost magical. Your work will be felt, not just measured. Every", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "fertility_family_building_benefits": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "adoption_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "childcare_subsidy": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10438780/principal-applied-scientist-personalization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "f56f38b1aabfd5be9880c83f022eccce", "title": "Senior Data Scientist, AI", "employer_name": "Ardent Health Inc", "employer_slug": "ardent-health", "location_text": "Remote - Nationwide", "country": "unknown", "employment_type": "unknown", "remote_status": "remote", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 0, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://ensemblehp.wd5.myworkdayjobs.com/EnsembleHealthPartnersCareers/job/Remote---Nationwide/Data-Scientist_R022351", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Data Scientist, AI Remote - Nationwide posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 48, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1756655/000162828026018174/ardt-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1756655/000162828026018174/ardt-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Healthcare", "employer_industry_source": "source_sector", "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.70e2342de5f1361023", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f56f38b1aabfd5be9880c83f022eccce", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f56f38b1aabfd5be9880c83f022eccce:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://ensemblehp.wd5.myworkdayjobs.com/EnsembleHealthPartnersCareers/job/Remote---Nationwide/Data-Scientist_R022351", "source_values": [ "rule:remote_status" ], "checked_at": null }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1756655/000162828026018174/ardt-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1756655/000162828026018174/ardt-20251231.htm" ], "checked_at": null }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://ensemblehp.wd5.myworkdayjobs.com/EnsembleHealthPartnersCareers/job/Remote---Nationwide/Data-Scientist_R022351", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://ensemblehp.wd5.myworkdayjobs.com/EnsembleHealthPartnersCareers/job/Remote---Nationwide/Data-Scientist_R022351", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "e78f8c8c4fb703b23d636bb0f22edee9", "title": "Senior Research Scientist, Machine Learning", "employer_name": "Analog Devices", "employer_slug": "analog-devices", "location_text": "US, MA, Wilmington", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp", "profit_share" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://analogdevices.wd1.myworkdayjobs.com/External/job/US-MA-Wilmington/Senior-Research-Scientist--Machine-Learning_R262487", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Research Scientist, Machine Learning US, MA, Wilmington posted: Posted 30+ Days Ago", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 77, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf", "db_column": "parental_leave_weeks", "source_url": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Technology", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.dad64ca6153bc4b7b5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "e78f8c8c4fb703b23d636bb0f22edee9", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:e78f8c8c4fb703b23d636bb0f22edee9:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/US-MA-Wilmington/Senior-Research-Scientist--Machine-Learning_R262487", "source_values": [ "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/US-MA-Wilmington/Senior-Research-Scientist--Machine-Learning_R262487", "source_values": [ "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/US-MA-Wilmington/Senior-Research-Scientist--Machine-Learning_R262487", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/US-MA-Wilmington/Senior-Research-Scientist--Machine-Learning_R262487", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "a82468c22792beb1eb357bc704831449", "title": "Site Reliability Engineer", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/bbd1a8e3-b943-4c8c-b239-e87951504f71", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Site Reliability Engineer San Francisco | OnSite SRE Location: San Francisco, CA (5 Days In-Office) You are the infrastructure expert who enables our rapid product development and guarantees 99.9%+ stability and performance of our clinical AI platform for major health systems. Your focus on operational excellence is directly tied to a patient's access to life-saving treatment. WHAT WE LOOK FOR IN A GREAT ENGINEER You have the intensity and technical mastery to own mission-critical infrastructure. You hold yourself and others to high standards and thrive in a high-energy, in-office culture where everyone is in it to win it. - Tool Proficiency: You are highly proficient with your tools-you speak command line fluently and have mastered keyboard shortcuts. - Ownership: You thrive on owning complex systems and have a proven track record of scaling mission-critical deployments. - Automation Drive: You love automating things, always finding new ways to increase your own leverage, and defining standards for operational excellence. - Problem Solver: You won't wait for someone else to solve a problem that you're in a position to solve; you are willing to jump into whatever needs to get done. WHAT YOU'LL WORK ON (RESPONSIBILITIES) As our SRE, you will own the entire production environment and improve the development experience: - Infrastructure Ownership: Design, implement, and maintain the production environment, having previously handled 500+ machine deployments. - Kubernetes Mastery: Own our containerized infrastructure, leveraging deep expertise in Kubernetes and Helm to manage deployment, scaling, and operational health. - CI/CD & Deployment Optimization:", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.c656763d6061b3e305", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a82468c22792beb1eb357bc704831449", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:a82468c22792beb1eb357bc704831449:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.9a521f8edd68b9dbd4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a82468c22792beb1eb357bc704831449", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bbd1a8e3-b943-4c8c-b239-e87951504f71", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bbd1a8e3-b943-4c8c-b239-e87951504f71", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bbd1a8e3-b943-4c8c-b239-e87951504f71", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/bbd1a8e3-b943-4c8c-b239-e87951504f71", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "c8313331e34874272e8196c151b5ae52", "title": "Junior Quantitative Analyst", "employer_name": "WorldQuant", "employer_slug": "worldquant", "location_text": "Austin, Texas", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 150000, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 150000, "base_salary_max": null, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-08T20:15:48.000Z", "apply_url": "https://job-boards.greenhouse.io/worldquant/jobs/4616499006", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Junior Quantitative Analyst Austin, Texas WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies - the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. The Role: - We seek candidates interested in being based in our Austin office to work alongside a Quantitative Portfolio Manager - The ideal candidate is a motivated junior quant researcher/developer with knowledge and interest at the intersection of financial markets, machine learning, and data engineering. - Key responsibilities include: - Searching for, understanding, and cleaning raw datasets from WQ's data library - Drawing on intuition about both finance and ML models to appropriately featurize data - Carrying out controlled experiments to discern the economic value of their features and feature combinations - Productionize features and models via", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "entry", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 0, "years_experience_max": 4, "role_function": "finance", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.996781434094c19b1c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "c8313331e34874272e8196c151b5ae52", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "finance", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:c8313331e34874272e8196c151b5ae52:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4616499006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4616499006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4616499006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/worldquant/jobs/4616499006", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "aa256fcdbbf611dbb2fcc867a9166e06", "title": "Member of Technical Staff - VLM", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "Freiburg (Germany)", "country": "DE", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": 130000, "salary_max": 340000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "EUR", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 130000, "base_salary_max": 340000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2026-04-20T09:34:33.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Member of Technical Staff - VLM Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX - foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role Vision-language models are becoming foundational to how people interact with generative AI - but most VLM research happens in isolation from the generation stack. At Black Forest Labs, we're integrating VLMs directly into FLUX in ways that make our models more powerful, more controllable, and more aligned with what creators actually want. This role is about pioneering that integration. You won't be applying off-the-shelf VLMs - you'll develop novel approaches, innovate on architectures, and answer questions that haven't been solved yet: how vision and language representations inform each other, how multimodal understanding improves generation quality, and how to make these capabilities deployable at scale without compromising what makes FLUX exceptional. This is a Staff / Senior IC role. We're looking for someone who has pretrained or significantly advanced a VLM, not just fine-tuned one. What You'll Work On - Lead development and training of state-of-the-art multimodal vision-language models within the FLUX stack - innovating", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "staff_plus", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.c7b68eccc0861f3a0f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "aa256fcdbbf611dbb2fcc867a9166e06", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - VLM Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX - foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role Vision-language models are becoming foundational to how people interact with generative AI - but most VLM research happens in isolation from the generation stack. At Black Forest Labs, we're integrating VLMs directly into FLUX in ways that make our models more powerful, more controllable, and more aligned with what creators actually want. This role is about pioneering that integration. You won't be applying off-the-shelf VLMs - you'll develop novel approaches, innovate on architectures, and answer questions that haven't been solved yet: how vision and language representations inform each other, how multimodal understanding improves generation quality, and how to make these capabilities deployable at scale without compromising what makes FLUX exceptional. This is a Staff / Senior IC role. We're looking for someone who has pretrained or significantly advanced a VLM, not just fine-tuned one. What You'll Work On - Lead development and training of state-of-the-art multimodal vision-language models within the FLUX stack - innovating", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:aa256fcdbbf611dbb2fcc867a9166e06:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.2d3942a837ae1374a3", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "aa256fcdbbf611dbb2fcc867a9166e06", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - VLM Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX - foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role Vision-language models are becoming foundational to how people interact with generative AI - but most VLM research happens in isolation from the generation stack. At Black Forest Labs, we're integrating VLMs directly into FLUX in ways that make our models more powerful, more controllable, and more aligned with what creators actually want. This role is about pioneering that integration. You won't be applying off-the-shelf VLMs - you'll develop novel approaches, innovate on architectures, and answer questions that haven't been solved yet: how vision and language representations inform each other, how multimodal understanding improves generation quality, and how to make these capabilities deployable at scale without compromising what makes FLUX exceptional. This is a Staff / Senior IC role. We're looking for someone who has pretrained or significantly advanced a VLM, not just fine-tuned one. What You'll Work On - Lead development and training of state-of-the-art multimodal vision-language models within the FLUX stack - innovating", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5193513008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "46a45542f22b45cae8ba32e1311cb51b", "title": "Senior Data Scientist", "employer_name": "Leidos", "employer_slug": "leidos", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://leidos.wd5.myworkdayjobs.com/External/job/Alexandria-VA/Senior-Data-Scientist_R-00179269", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Data Scientist 2 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": 4, "non_birth_parent_leave_weeks": 4, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.leidos.com/careers/pay-benefits", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 73, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1336920/000133692026000030/ldos-20260102.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1336920/000133692026000030/ldos-20260102.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.leidos.com/careers/pay-benefits", "db_column": "parental_leave_weeks", "source_url": "https://www.leidos.com/careers/pay-benefits" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://www.leidos.com/careers/pay-benefits", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://www.leidos.com/careers/pay-benefits" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Defense", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.c83dae46e93a8664b5", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "46a45542f22b45cae8ba32e1311cb51b", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:46a45542f22b45cae8ba32e1311cb51b:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1336920/000133692026000030/ldos-20260102.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1336920/000133692026000030/ldos-20260102.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://leidos.wd5.myworkdayjobs.com/External/job/Alexandria-VA/Senior-Data-Scientist_R-00179269", "source_values": [ "https://www.leidos.com/careers/pay-benefits" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://leidos.wd5.myworkdayjobs.com/External/job/Alexandria-VA/Senior-Data-Scientist_R-00179269", "source_values": [ "https://www.leidos.com/careers/pay-benefits" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://leidos.wd5.myworkdayjobs.com/External/job/Alexandria-VA/Senior-Data-Scientist_R-00179269", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://leidos.wd5.myworkdayjobs.com/External/job/Alexandria-VA/Senior-Data-Scientist_R-00179269", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "aa32db15d2a867591a0c387ae0afd75e", "title": "Principal Technical Sourcer", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | Hybrid | Remote", "country": "US", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/0c96c498-7c45-4589-b9bc-e6cf2dc9c5f4", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Principal Technical Sourcer San Francisco | Hybrid | Remote TECHNICAL SOURCER We're building a high-caliber technical team in a competitive market. This role owns the top of the funnel-finding, engaging, and building relationships with exceptional engineers before they're actively looking. This is not a resume-pushing role. It's about taste, curiosity, and persistence. WHAT YOU'LL DO - Own outbound sourcing for technical roles (primarily software engineers) - Identify high-signal candidates via LinkedIn, GitHub, referrals, and communities - Write thoughtful, personalized outreach that actually gets replies - Partner closely with recruiters and hiring managers to define “great” - Experiment with sourcing strategies, messaging, and channels - Track and improve funnel metrics (reply rate, pass-through, time to first convo) - Build long-term candidate relationships, even when timing isn't right YOU MIGHT BE A GOOD FIT IF - You have 1-4+ years of technical sourcing experience (startup experience a plus) - You enjoy the puzzle of finding people others can't - You care about quality over volume and hate spammy outreach - You're comfortable with ambiguity and like building from scratch - You operate with urgency and take ownership - Bias to action WHAT SUCCESS LOOKS LIKE - Consistent flow of high-quality technical candidates - Strong reply rates driven by thoughtful outreach - Hiring managers trust your judgment and taste WHY JOIN - Real ownership and visibility - Opportunity to help define the sourcing playbook - Close partnership with recruiters and founders - High impact on a team scaling quickly - Exposure to the", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "principal", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 1, "years_experience_max": 12, "role_function": "teaching_education", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "series_a", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.4fbcf1e96676438504", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "aa32db15d2a867591a0c387ae0afd75e", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:aa32db15d2a867591a0c387ae0afd75e:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.3754813c56eb8c5432", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "aa32db15d2a867591a0c387ae0afd75e", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/0c96c498-7c45-4589-b9bc-e6cf2dc9c5f4", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/0c96c498-7c45-4589-b9bc-e6cf2dc9c5f4", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/0c96c498-7c45-4589-b9bc-e6cf2dc9c5f4", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/0c96c498-7c45-4589-b9bc-e6cf2dc9c5f4", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "bb0d04218a8da3317164841ffa0351b7", "title": "Software Development Manager, CreativeX - Dynamic Creative Optimization", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "New York, New York, USA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 203400, "salary_max": 275200, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 203400, "base_salary_max": 275200, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-09T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Development Manager, CreativeX - Dynamic Creative Optimization New York, New York, USA The CreativeX RAPID(Real-time Ad Personalization & Insights Development) team is seeking passionate and talented SDM to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - As a Software Engineering Manager you will be responsible for leading a strong team of engineers to innovate and deliver software and infrastructure that powers the Dynamic Creative Optimization. - Work backwards from the customer to invent new solutions and to improve existing software. - Work with the product and technical leaders to define the product direction and roadmap. - Create vision and execution documents. Insist on the high bar for technical documentation and the quality of the developed software. - Perform hands-on code reviews and other technical deep dives to ensure the right thing is being built and delivered to customers. - Hire, coach, and mentor software engineers. About", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 56, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 8, "years_experience_max": 9, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.b619b144feb06a8c7b", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "bb0d04218a8da3317164841ffa0351b7", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Development Manager, CreativeX - Dynamic Creative Optimization New York, New York, USA The CreativeX RAPID(Real-time Ad Personalization & Insights Development) team is seeking passionate and talented SDM to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - As a Software Engineering Manager you will be responsible for leading a strong team of engineers to innovate and deliver software and infrastructure that powers the Dynamic Creative Optimization. - Work backwards from the customer to invent new solutions and to improve existing software. - Work with the product and technical leaders to define the product direction and roadmap. - Create vision and execution documents. Insist on the high bar for technical documentation and the quality of the developed software. - Perform hands-on code reviews and other technical deep dives to ensure the right thing is being built and delivered to customers. - Hire, coach, and mentor software engineers. About", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:bb0d04218a8da3317164841ffa0351b7:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.33931214630718337c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "bb0d04218a8da3317164841ffa0351b7", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Development Manager, CreativeX - Dynamic Creative Optimization New York, New York, USA The CreativeX RAPID(Real-time Ad Personalization & Insights Development) team is seeking passionate and talented SDM to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - As a Software Engineering Manager you will be responsible for leading a strong team of engineers to innovate and deliver software and infrastructure that powers the Dynamic Creative Optimization. - Work backwards from the customer to invent new solutions and to improve existing software. - Work with the product and technical leaders to define the product direction and roadmap. - Create vision and execution documents. Insist on the high bar for technical documentation and the quality of the developed software. - Perform hands-on code reviews and other technical deep dives to ensure the right thing is being built and delivered to customers. - Hire, coach, and mentor software engineers. About", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "fertility_family_building_benefits": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "adoption_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "childcare_subsidy": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10444034/software-development-manager-creativex-dynamic-creative-optimization", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "0fb3a7aef366eadf293aa7972cb5ab22", "title": "Senior Estimator", "employer_name": "Johnson Controls", "employer_slug": "johnson-controls", "location_text": "8 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://jci.wd5.myworkdayjobs.com/JCI/job/Birmingham-Midlands-United-Kingdom/Senior-Estimator_WD30256739", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Estimator 8 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/833444/000083344425000097/jci-20250930.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/833444/000083344425000097/jci-20250930.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.9c93755675559d99da", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0fb3a7aef366eadf293aa7972cb5ab22", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:0fb3a7aef366eadf293aa7972cb5ab22:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/833444/000083344425000097/jci-20250930.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/833444/000083344425000097/jci-20250930.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jci.wd5.myworkdayjobs.com/JCI/job/Birmingham-Midlands-United-Kingdom/Senior-Estimator_WD30256739", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "68f51b4fc29b8383df5d2290a6214413", "title": "Transformation Senior Consultant", "employer_name": "Baker Tilly", "employer_slug": "baker-tilly", "location_text": "4 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://bakertilly.wd5.myworkdayjobs.com/BTCareers/job/USA-PA-Philadelphia/Transformation-Senior-Consultant_JR105617", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Transformation Senior Consultant 4 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.c85c6b50130a4d525f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "68f51b4fc29b8383df5d2290a6214413", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:68f51b4fc29b8383df5d2290a6214413:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://bakertilly.wd5.myworkdayjobs.com/BTCareers/job/USA-PA-Philadelphia/Transformation-Senior-Consultant_JR105617", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://bakertilly.wd5.myworkdayjobs.com/BTCareers/job/USA-PA-Philadelphia/Transformation-Senior-Consultant_JR105617", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "becd4ae7bf95b685b69d8384b632a88e", "title": "Business Generalist", "employer_name": "Parloa", "employer_slug": "parloa", "location_text": "New York Office", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 150000, "salary_max": 300000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "ote", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 150000, "base_salary_max": 300000, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn" ], "posted_at": "2026-04-01T20:48:02.000Z", "apply_url": "https://job-boards.eu.greenhouse.io/parloa/jobs/4829912101", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Business Generalist New York Office About Parloa Parloa's mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience - one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com HealthEquity, Allianz, SAP, BarmeniaGothaer and TUI already deploying Parloa at scale. About the role: As we enter our next phase of hyper-growth, we are seeking elite business generalists to join and grow a range of our functions. This is a high-bandwidth, high-visibility role designed for highly driven candidates who thrive on ambiguity and possess an “operator mindset.\" This is a multi-disciplinary leadership track. Rather than slotting you into a rigid functional box today, we hire for raw talent, rigorous thinking, and extreme ownership. Based on your specific \"superpower\" and our priorities, you will be deployed into one of three critical functions: Chief of Staff, Business Operations, or Strategic Finance. Areas of ownership: Depending on your background and Parloa's strategic roadmap, you will own one of the following domains: - Chief of Staff: Act as the high-bandwidth force multiplier and strategic architect for the executive team. You will spearhead \"Zero-to-One\" breakthroughs while serving as the primary connective tissue across the organization. By identifying latent cross-functional risks and synchronizing disparate workstreams, you ensure our most", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 4, "years_experience_max": 12, "role_function": "operations", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.2b89480c3e8b1d9ea8", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "becd4ae7bf95b685b69d8384b632a88e", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Business Generalist New York Office About Parloa Parloa's mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience - one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com HealthEquity, Allianz, SAP, BarmeniaGothaer and TUI already deploying Parloa at scale. About the role: As we enter our next phase of hyper-growth, we are seeking elite business generalists to join and grow a range of our functions. This is a high-bandwidth, high-visibility role designed for highly driven candidates who thrive on ambiguity and possess an “operator mindset.\" This is a multi-disciplinary leadership track. Rather than slotting you into a rigid functional box today, we hire for raw talent, rigorous thinking, and extreme ownership. Based on your specific \"superpower\" and our priorities, you will be deployed into one of three critical functions: Chief of Staff, Business Operations, or Strategic Finance. Areas of ownership: Depending on your background and Parloa's strategic roadmap, you will own one of the following domains: - Chief of Staff: Act as the high-bandwidth force multiplier and strategic architect for the executive team. You will spearhead \"Zero-to-One\" breakthroughs while serving as the primary connective tissue across the organization. By identifying latent cross-functional risks and synchronizing disparate workstreams, you ensure our most", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:becd4ae7bf95b685b69d8384b632a88e:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.52d668b1f3f0e28f54", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "becd4ae7bf95b685b69d8384b632a88e", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Business Generalist New York Office About Parloa Parloa's mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI accelerates, Parloans are shaping the foundation of a new era in customer experience - one where customer support is no longer transactions, but meaningful exchanges. It is not just a vision; Parloa has powered over ONE BILLION interactions between global enterprise brands and their customers, with companies like Booking.com HealthEquity, Allianz, SAP, BarmeniaGothaer and TUI already deploying Parloa at scale. About the role: As we enter our next phase of hyper-growth, we are seeking elite business generalists to join and grow a range of our functions. This is a high-bandwidth, high-visibility role designed for highly driven candidates who thrive on ambiguity and possess an “operator mindset.\" This is a multi-disciplinary leadership track. Rather than slotting you into a rigid functional box today, we hire for raw talent, rigorous thinking, and extreme ownership. Based on your specific \"superpower\" and our priorities, you will be deployed into one of three critical functions: Chief of Staff, Business Operations, or Strategic Finance. Areas of ownership: Depending on your background and Parloa's strategic roadmap, you will own one of the following domains: - Chief of Staff: Act as the high-bandwidth force multiplier and strategic architect for the executive team. You will spearhead \"Zero-to-One\" breakthroughs while serving as the primary connective tissue across the organization. By identifying latent cross-functional risks and synchronizing disparate workstreams, you ensure our most", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.eu.greenhouse.io/parloa/jobs/4829912101", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.eu.greenhouse.io/parloa/jobs/4829912101", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.eu.greenhouse.io/parloa/jobs/4829912101", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.eu.greenhouse.io/parloa/jobs/4829912101", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.eu.greenhouse.io/parloa/jobs/4829912101", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "350359ccc3ea832f4eb2f933488af777", "title": "Senior Machine Learning Engineer III", "employer_name": "RELX", "employer_slug": "relx", "location_text": "Mexico", "country": "MX", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-04T00:00:00.000Z", "apply_url": "https://relx.wd3.myworkdayjobs.com/relx/job/Mexico/Senior-Machine-Learning-Engineer-III_R110959-2", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer III Mexico posted: Posted 8 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.f03547c9c1d7d50633", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "350359ccc3ea832f4eb2f933488af777", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:350359ccc3ea832f4eb2f933488af777:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://relx.wd3.myworkdayjobs.com/relx/job/Mexico/Senior-Machine-Learning-Engineer-III_R110959-2", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://relx.wd3.myworkdayjobs.com/relx/job/Mexico/Senior-Machine-Learning-Engineer-III_R110959-2", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "f0e8e27418057e64db77a18b4acffdf9", "title": "Software Engineer", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite | New York City", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/37081ba7-cd44-4a40-bc5f-4a2829c0d079", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Engineer San Francisco | OnSite | New York City SOFTWARE ENGINEER Location: San Francisco, CA We're building the intelligence layer of American healthcare. Our systems sit directly in critical workflows and power how clinical AI is delivered and used in practice. The work here translates into faster access to treatment and significantly less operational burden for healthcare teams. You'll be working on the core systems that make this possible. WHAT WE LOOK FOR IN A GREAT ENGINEER You're a builder who holds yourself and others to high standards. You speak command line fluently, live and die by keyboard shortcuts, and are constantly looking for ways to increase your leverage through automation. This is a high-energy environment for people who want to build real systems that matter. - Ownership: You don't hand off problems. You own them end to end, from understanding the problem to shipping production systems. - Bias for Action: You won't wait for someone else to solve a problem you're in a position to solve. - Automation Mindset: You're always looking for ways to move faster by automating repetitive or manual work. - Complexity Management: You're good at building simple, durable abstractions over messy, real-world systems. - Product Focus: You're comfortable working close to the product, talking to users, shaping requirements, and iterating quickly. WHAT YOU'LL WORK ON You'll primarily focus on backend systems, while contributing across the stack when needed to ship complete features. - Backend Systems & Infrastructure Design and build high-performance, reliable systems that", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.6474146de55ddf37d1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f0e8e27418057e64db77a18b4acffdf9", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f0e8e27418057e64db77a18b4acffdf9:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.7afa22702705c8f92e", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f0e8e27418057e64db77a18b4acffdf9", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/37081ba7-cd44-4a40-bc5f-4a2829c0d079", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/37081ba7-cd44-4a40-bc5f-4a2829c0d079", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/37081ba7-cd44-4a40-bc5f-4a2829c0d079", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/37081ba7-cd44-4a40-bc5f-4a2829c0d079", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "03007afa9856ce6e77a6c0d07bb00491", "title": "Account Executive", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "Freiburg im Breisgau, Baden-Württemberg (Germany), San Francisco, California (United States)", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": 130000, "salary_max": 180000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 130000, "base_salary_max": 180000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2026-03-05T08:58:18.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Account Executive Freiburg im Breisgau, Baden-Württemberg (Germany), San Francisco, California (United States) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role Our models are seeing strong demand across startups, AI-native companies, and some of the world's most recognized brands. We need someone who can turn that demand into revenue - fast, consistently, and across a high volume of accounts. This is a full-cycle role: you own everything from first touch to close to renewal and upsell. Some of your accounts will be fast-moving AI startups. Others will be household-name enterprises starting with a focused use case - and it's your job to land them, prove value, and grow them into strategic relationships. If you're already closing deals and want to be at the forefront of the generative AI wave, this is the role. What You'll Work On - Own the full sales cycle from qualification through close for SMB, startup, AI-native, and enterprise customers - including well-known global brands entering generative", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "sales", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.36c2cd17125bcbd729", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "03007afa9856ce6e77a6c0d07bb00491", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Account Executive Freiburg im Breisgau, Baden-Württemberg (Germany), San Francisco, California (United States) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role Our models are seeing strong demand across startups, AI-native companies, and some of the world's most recognized brands. We need someone who can turn that demand into revenue - fast, consistently, and across a high volume of accounts. This is a full-cycle role: you own everything from first touch to close to renewal and upsell. Some of your accounts will be fast-moving AI startups. Others will be household-name enterprises starting with a focused use case - and it's your job to land them, prove value, and grow them into strategic relationships. If you're already closing deals and want to be at the forefront of the generative AI wave, this is the role. What You'll Work On - Own the full sales cycle from qualification through close for SMB, startup, AI-native, and enterprise customers - including well-known global brands entering generative", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:03007afa9856ce6e77a6c0d07bb00491:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.fc2d52f6be0e644ba6", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "03007afa9856ce6e77a6c0d07bb00491", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Account Executive Freiburg im Breisgau, Baden-Württemberg (Germany), San Francisco, California (United States) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role Our models are seeing strong demand across startups, AI-native companies, and some of the world's most recognized brands. We need someone who can turn that demand into revenue - fast, consistently, and across a high volume of accounts. This is a full-cycle role: you own everything from first touch to close to renewal and upsell. Some of your accounts will be fast-moving AI startups. Others will be household-name enterprises starting with a focused use case - and it's your job to land them, prove value, and grow them into strategic relationships. If you're already closing deals and want to be at the forefront of the generative AI wave, this is the role. What You'll Work On - Own the full sales cycle from qualification through close for SMB, startup, AI-native, and enterprise customers - including well-known global brands entering generative", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5142062008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "e61b7e2f726eaa137dc263b70ae948f4", "title": "Principal, Machine Learning Scientist", "employer_name": "Cotality", "employer_slug": "cotality", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://cotality.wd115.myworkdayjobs.com/Global/job/Dallas---3001-Hackberry-Rd/Principal--Data-Scientist_REQ17401", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Principal, Machine Learning Scientist 2 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "staff_plus", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 8, "years_experience_max": 12, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.f4c6b62b27394256f7", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "e61b7e2f726eaa137dc263b70ae948f4", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:e61b7e2f726eaa137dc263b70ae948f4:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://cotality.wd115.myworkdayjobs.com/Global/job/Dallas---3001-Hackberry-Rd/Principal--Data-Scientist_REQ17401", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://cotality.wd115.myworkdayjobs.com/Global/job/Dallas---3001-Hackberry-Rd/Principal--Data-Scientist_REQ17401", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "6001d1b12dea3cc458d4a840e6ef3c76", "title": "Machine Learning Engineer", "employer_name": "Huron Consulting Group Inc", "employer_slug": "huron-consulting-group", "location_text": "Belfast - 20 Adelaide Street", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://huron.wd1.myworkdayjobs.com/huroncareers/job/Belfast---20-Adelaide-Street/Machine-Learning-Engineer_JR-0013617", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Engineer Belfast - 20 Adelaide Street posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1289848/000162828026011093/hurn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1289848/000162828026011093/hurn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Professional Services", "employer_industry_source": "source_sector", "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.1f332d5bb16436f9e1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "6001d1b12dea3cc458d4a840e6ef3c76", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:6001d1b12dea3cc458d4a840e6ef3c76:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1289848/000162828026011093/hurn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1289848/000162828026011093/hurn-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://huron.wd1.myworkdayjobs.com/huroncareers/job/Belfast---20-Adelaide-Street/Machine-Learning-Engineer_JR-0013617", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://huron.wd1.myworkdayjobs.com/huroncareers/job/Belfast---20-Adelaide-Street/Machine-Learning-Engineer_JR-0013617", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "0a4cb1dcb727faa0bf7cac71173e9429", "title": "HPC Data Center Technician", "employer_name": "Jump Trading", "employer_slug": "jump-trading", "location_text": "Carrollton, TX", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 75000, "salary_max": 90000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 75000, "base_salary_max": 90000, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-04-15T21:42:51.000Z", "apply_url": "https://www.jumptrading.com/hr/job?gh_jid=7781600", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "HPC Data Center Technician Carrollton, TX Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Jump's Trading Infrastructure team is responsible for the datacenters, networks and supporting systems that power both Jump's global low latency environment and high-performance computing (HPC) clusters. In this role you'll have an opportunity to work with the latest in server, network and storage technology such as high-density cabinets and liquid cooling. You'll also exist in a capacity that will allow project ownership from start to finish. What You Will Do: - Maintain data center infrastructure (power, cooling, hardware, cabling, tools/supplies, etc.). to provide maximum availability of", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "weekend_work": { "field": "weekend_work", "source": "llm:greenfield:inv_371_postfix", "db_column": "weekend_work" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.a96e752893a1b299d6", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0a4cb1dcb727faa0bf7cac71173e9429", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "HPC Data Center Technician Carrollton, TX Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Jump's Trading Infrastructure team is responsible for the datacenters, networks and supporting systems that power both Jump's global low latency environment and high-performance computing (HPC) clusters. In this role you'll have an opportunity to work with the latest in server, network and storage technology such as high-density cabinets and liquid cooling. You'll also exist in a capacity that will allow project ownership from start to finish. What You Will Do: - Maintain data center infrastructure (power, cooling, hardware, cabling, tools/supplies, etc.). to provide maximum availability of", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:0a4cb1dcb727faa0bf7cac71173e9429:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.b90e962607fa40c6e3", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0a4cb1dcb727faa0bf7cac71173e9429", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "HPC Data Center Technician Carrollton, TX Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Jump's Trading Infrastructure team is responsible for the datacenters, networks and supporting systems that power both Jump's global low latency environment and high-performance computing (HPC) clusters. In this role you'll have an opportunity to work with the latest in server, network and storage technology such as high-density cabinets and liquid cooling. You'll also exist in a capacity that will allow project ownership from start to finish. What You Will Do: - Maintain data center infrastructure (power, cooling, hardware, cabling, tools/supplies, etc.). to provide maximum availability of", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781600", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781600", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781600", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781600", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781600", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "9478389ec219ad4374a646e3dc664cdb", "title": "Senior Machine Learning Engineer", "employer_name": "Adobe Inc.", "employer_slug": "adobe", "location_text": "3 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Francisco/Senior-Machine-Learning-Engineer_R166334", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer 3 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": 16, "non_birth_parent_leave_weeks": 16, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://benefits.adobe.com/us/time-off/leaves-of-absence", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 90, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://benefits.adobe.com/us/time-off/leaves-of-absence", "db_column": "parental_leave_weeks", "source_url": "https://benefits.adobe.com/us/time-off/leaves-of-absence" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://benefits.adobe.com/us/time-off/leaves-of-absence", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://benefits.adobe.com/us/time-off/leaves-of-absence" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Technology", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.c8d206481970e7fba0", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "9478389ec219ad4374a646e3dc664cdb", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:9478389ec219ad4374a646e3dc664cdb:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Francisco/Senior-Machine-Learning-Engineer_R166334", "source_values": [ "https://benefits.adobe.com/us/time-off/leaves-of-absence" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Francisco/Senior-Machine-Learning-Engineer_R166334", "source_values": [ "https://benefits.adobe.com/us/time-off/leaves-of-absence" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Francisco/Senior-Machine-Learning-Engineer_R166334", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Francisco/Senior-Machine-Learning-Engineer_R166334", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "19ccce00c89a81357b215c20ea393deb", "title": "Member of Technical Staff - Image / Video Generation", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "Freiburg (Germany)", "country": "DE", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": 130000, "salary_max": 340000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "EUR", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 130000, "base_salary_max": 340000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2024-09-17T04:14:50.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Member of Technical Staff - Image / Video Generation Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes-it's about running the experiments that clarify which architectural choices matter and which are less impactful. What You'll Work On - Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters - Rigorously ablates design choices-running experiments that isolate variables, control for confounds, and produce insights you can actually trust-then communicating those results to shape our research direction - Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously - Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough What", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "staff_plus", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.e038111756505e2806", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "19ccce00c89a81357b215c20ea393deb", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - Image / Video Generation Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes-it's about running the experiments that clarify which architectural choices matter and which are less impactful. What You'll Work On - Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters - Rigorously ablates design choices-running experiments that isolate variables, control for confounds, and produce insights you can actually trust-then communicating those results to shape our research direction - Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously - Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough What", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:19ccce00c89a81357b215c20ea393deb:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.fc9799c64719946385", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "19ccce00c89a81357b215c20ea393deb", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - Image / Video Generation Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role You'll train large-scale diffusion models for image and video generation, exploring new approaches while maintaining the rigor that helps us distinguish meaningful progress from incremental tweaks. This isn't about following established recipes-it's about running the experiments that clarify which architectural choices matter and which are less impactful. What You'll Work On - Trains large-scale diffusion transformer models for image and video data, working at the scale where intuitions break and empirical evidence matters - Rigorously ablates design choices-running experiments that isolate variables, control for confounds, and produce insights you can actually trust-then communicating those results to shape our research direction - Reasons about the speed-quality tradeoffs of neural network architectures in production settings where both constraints matter simultaneously - Fine-tunes diffusion models for specialized applications like image and video upscalers, inpainting/outpainting models, and other tasks where general-purpose models aren't enough What", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4132217008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "22fd0b1381fa2b87b3865e594618396e", "title": "World Model Research Scientist- Physical AI", "employer_name": "Kodiak Sciences", "employer_slug": "kodiak-sciences", "location_text": "Mountain View, CA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 190000, "salary_max": 250000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base_plus_commission", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 190000, "base_salary_max": 250000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-04T16:41:40.000Z", "apply_url": "https://job-boards.greenhouse.io/kodiak/jobs/4203253009", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "World Model Research Scientist- Physical AI Mountain View, CA Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense. Kodiak is building AI that doesn't just perceive the world, it learns how the physics of the world works. We are developing large-scale generative world models that learn to predict realistic, physically consistent futures from real-world sensor data. This capability serves as the foundation for scalable closed-loop training, validation, and long-tail scenario generation, and is distilled into the onboard models that drive our autonomous trucks. We are looking for a research scientist to lead the design and development of world models capable of generating multi-sensor, multi-view, temporally coherent driving scenarios conditioned on actions, 3D scene context, and text. In this role, you will: Design and train generative world models that synthesize realistic multi-camera video and LiDAR conditioned on ego trajectories, 3D scene context, and text Research and implement conditional diffusion architectures for driving, including spatiotemporal attention, latent space design, and", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1468748/000119312526134887/kod-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1468748/000119312526134887/kod-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Energy", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.b7083ade629338f320", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "22fd0b1381fa2b87b3865e594618396e", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "World Model Research Scientist- Physical AI Mountain View, CA Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense. Kodiak is building AI that doesn't just perceive the world, it learns how the physics of the world works. We are developing large-scale generative world models that learn to predict realistic, physically consistent futures from real-world sensor data. This capability serves as the foundation for scalable closed-loop training, validation, and long-tail scenario generation, and is distilled into the onboard models that drive our autonomous trucks. We are looking for a research scientist to lead the design and development of world models capable of generating multi-sensor, multi-view, temporally coherent driving scenarios conditioned on actions, 3D scene context, and text. In this role, you will: Design and train generative world models that synthesize realistic multi-camera video and LiDAR conditioned on ego trajectories, 3D scene context, and text Research and implement conditional diffusion architectures for driving, including spatiotemporal attention, latent space design, and", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:22fd0b1381fa2b87b3865e594618396e:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.cb90e3602a035793d6", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "22fd0b1381fa2b87b3865e594618396e", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "World Model Research Scientist- Physical AI Mountain View, CA Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense. Kodiak is building AI that doesn't just perceive the world, it learns how the physics of the world works. We are developing large-scale generative world models that learn to predict realistic, physically consistent futures from real-world sensor data. This capability serves as the foundation for scalable closed-loop training, validation, and long-tail scenario generation, and is distilled into the onboard models that drive our autonomous trucks. We are looking for a research scientist to lead the design and development of world models capable of generating multi-sensor, multi-view, temporally coherent driving scenarios conditioned on actions, 3D scene context, and text. In this role, you will: Design and train generative world models that synthesize realistic multi-camera video and LiDAR conditioned on ego trajectories, 3D scene context, and text Research and implement conditional diffusion architectures for driving, including spatiotemporal attention, latent space design, and", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/kodiak/jobs/4203253009", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/kodiak/jobs/4203253009", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/kodiak/jobs/4203253009", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1468748/000119312526134887/kod-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1468748/000119312526134887/kod-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/kodiak/jobs/4203253009", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/kodiak/jobs/4203253009", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "db585fa4cf2e616dc32e9e76e23affd3", "title": "Senior Statistician II", "employer_name": "AstraZeneca", "employer_slug": "astrazeneca", "location_text": "Spain - Barcelona", "country": "ES", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T00:00:00.000Z", "apply_url": "https://astrazeneca.wd3.myworkdayjobs.com/Careers/job/Spain---Barcelona/Senior-Statistician-II_R-253223", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Statistician II Spain - Barcelona posted: Posted Yesterday", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.294c78a04177dfa026", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "db585fa4cf2e616dc32e9e76e23affd3", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:db585fa4cf2e616dc32e9e76e23affd3:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://astrazeneca.wd3.myworkdayjobs.com/Careers/job/Spain---Barcelona/Senior-Statistician-II_R-253223", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null } } }, { "id": "48d067554d27c144a68402805a047854", "title": "Data Scientist", "employer_name": "Medtronic", "employer_slug": "medtronic", "location_text": "Heerlen, Limburg, Netherlands", "country": "NL", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T00:00:00.000Z", "apply_url": "https://medtronic.wd1.myworkdayjobs.com/medtroniccareers/job/Heerlen-Limburg-Netherlands/Data-Scientist_R68156", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Data Scientist Heerlen, Limburg, Netherlands posted: Posted Yesterday", "parental_leave_weeks": 24, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://news.medtronic.com/9-benefits-Medtronic-offers-to-support-you-and-your-loved-ones-newsroom", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 65, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1613103/000162828026044354/mdt-20260424.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1613103/000162828026044354/mdt-20260424.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://news.medtronic.com/9-benefits-Medtronic-offers-to-support-you-and-your-loved-ones-newsroom", "db_column": "parental_leave_weeks", "source_url": "https://news.medtronic.com/9-benefits-Medtronic-offers-to-support-you-and-your-loved-ones-newsroom" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Healthcare", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.78abce75e5b7543959", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "48d067554d27c144a68402805a047854", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:48d067554d27c144a68402805a047854:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1613103/000162828026044354/mdt-20260424.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1613103/000162828026044354/mdt-20260424.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://medtronic.wd1.myworkdayjobs.com/medtroniccareers/job/Heerlen-Limburg-Netherlands/Data-Scientist_R68156", "source_values": [ "https://news.medtronic.com/9-benefits-Medtronic-offers-to-support-you-and-your-loved-ones-newsroom" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://medtronic.wd1.myworkdayjobs.com/medtroniccareers/job/Heerlen-Limburg-Netherlands/Data-Scientist_R68156", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://medtronic.wd1.myworkdayjobs.com/medtroniccareers/job/Heerlen-Limburg-Netherlands/Data-Scientist_R68156", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "27a9ff175bc105410e85ca8ff335a6ea", "title": "AI Scientist - Biomedical Multimodal Modeling", "employer_name": "Xaira", "employer_slug": "xaira", "location_text": "South San Francisco, California, United States", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 170000, "salary_max": 240000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 170000, "base_salary_max": 240000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-04T20:02:38.000Z", "apply_url": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "AI Scientist - Biomedical Multimodal Modeling South San Francisco, California, United States About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London. About the Role We are building foundation models for biological systems, focusing on learning from complex, multimodal data. Our approach combines representation learning and generative modeling to capture structure, variation, and latent organization in high-dimensional settings. We work with both image-based data and high-dimensional molecular (omics) measurements, treating them as complementary views of the same underlying system. Our goal is to develop models that can both encode rich representations and generate or infer missing aspects from partial observations. What You'll Do - Develop multimodal foundation models spanning image and molecular data - Design approaches that unify representation learning and generative modeling - Train large-scale models on structured, high-dimensional datasets - Explore cross-modal alignment, prediction, and generation - Build scalable systems for data processing, training, and evaluation - Work in a research-driven environment with evolving problem definitions What We're Looking For Strong background in deep", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "seed", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.51a16fea836f8198c2", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "27a9ff175bc105410e85ca8ff335a6ea", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "AI Scientist - Biomedical Multimodal Modeling South San Francisco, California, United States About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London. About the Role We are building foundation models for biological systems, focusing on learning from complex, multimodal data. Our approach combines representation learning and generative modeling to capture structure, variation, and latent organization in high-dimensional settings. We work with both image-based data and high-dimensional molecular (omics) measurements, treating them as complementary views of the same underlying system. Our goal is to develop models that can both encode rich representations and generate or infer missing aspects from partial observations. What You'll Do - Develop multimodal foundation models spanning image and molecular data - Design approaches that unify representation learning and generative modeling - Train large-scale models on structured, high-dimensional datasets - Explore cross-modal alignment, prediction, and generation - Build scalable systems for data processing, training, and evaluation - Work in a research-driven environment with evolving problem definitions What We're Looking For Strong background in deep", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:27a9ff175bc105410e85ca8ff335a6ea:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.02f8b8af5302029e15", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "27a9ff175bc105410e85ca8ff335a6ea", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "AI Scientist - Biomedical Multimodal Modeling South San Francisco, California, United States About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London. About the Role We are building foundation models for biological systems, focusing on learning from complex, multimodal data. Our approach combines representation learning and generative modeling to capture structure, variation, and latent organization in high-dimensional settings. We work with both image-based data and high-dimensional molecular (omics) measurements, treating them as complementary views of the same underlying system. Our goal is to develop models that can both encode rich representations and generate or infer missing aspects from partial observations. What You'll Do - Develop multimodal foundation models spanning image and molecular data - Design approaches that unify representation learning and generative modeling - Train large-scale models on structured, high-dimensional datasets - Explore cross-modal alignment, prediction, and generation - Build scalable systems for data processing, training, and evaluation - Work in a research-driven environment with evolving problem definitions What We're Looking For Strong background in deep", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/xairatherapeutics/jobs/5036777007", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "8fd547752811919aceded7a9bbdb2c77", "title": "Senior Machine Learning Engineer", "employer_name": "Toronto-Dominion Bank", "employer_slug": "toronto-dominion-bank", "location_text": "Toronto, Ontario", "country": "CA", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://td.wd3.myworkdayjobs.com/TD_Bank_Careers/job/Toronto-Ontario/Senior-Machine-Learning-Engineer_R_1478728", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer Toronto, Ontario posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.769a3a135bc5b47fbe", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8fd547752811919aceded7a9bbdb2c77", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8fd547752811919aceded7a9bbdb2c77:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://td.wd3.myworkdayjobs.com/TD_Bank_Careers/job/Toronto-Ontario/Senior-Machine-Learning-Engineer_R_1478728", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://td.wd3.myworkdayjobs.com/TD_Bank_Careers/job/Toronto-Ontario/Senior-Machine-Learning-Engineer_R_1478728", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "283c21288bddb2cff9bbda4be0de3bec", "title": "Data Center Engineer", "employer_name": "Jump Trading", "employer_slug": "jump-trading", "location_text": "Mumbai", "country": "IN", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-15T05:15:57.000Z", "apply_url": "https://www.jumptrading.com/hr/job?gh_jid=7715291", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Data Center Engineer Mumbai Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Production Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Jump's Production Infrastructure team is responsible for the datacenters, networks and supporting systems that power both Jump's global low latency environment and high-performance computing (HPC) clusters. In this role you'll have an opportunity to work with the latest in server, network and storage technology such as high-density cabinets and liquid cooling. You'll also exist in a capacity that will allow project ownership from start to finish. What You'll Do: - Remotely manage data center infrastructure (power, cooling, hardware, cabling, tools/supplies, etc.) to provide maximum availability of services. -", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 2, "years_experience_max": 7, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.6877939a47685966fe", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "283c21288bddb2cff9bbda4be0de3bec", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Data Center Engineer Mumbai Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Production Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Jump's Production Infrastructure team is responsible for the datacenters, networks and supporting systems that power both Jump's global low latency environment and high-performance computing (HPC) clusters. In this role you'll have an opportunity to work with the latest in server, network and storage technology such as high-density cabinets and liquid cooling. You'll also exist in a capacity that will allow project ownership from start to finish. What You'll Do: - Remotely manage data center infrastructure (power, cooling, hardware, cabling, tools/supplies, etc.) to provide maximum availability of services. -", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:283c21288bddb2cff9bbda4be0de3bec:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.763cdee638250dd98a", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "283c21288bddb2cff9bbda4be0de3bec", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Data Center Engineer Mumbai Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Production Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Jump's Production Infrastructure team is responsible for the datacenters, networks and supporting systems that power both Jump's global low latency environment and high-performance computing (HPC) clusters. In this role you'll have an opportunity to work with the latest in server, network and storage technology such as high-density cabinets and liquid cooling. You'll also exist in a capacity that will allow project ownership from start to finish. What You'll Do: - Remotely manage data center infrastructure (power, cooling, hardware, cabling, tools/supplies, etc.) to provide maximum availability of services. -", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7715291", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7715291", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "2ce24d6b2e69f533019c701ab2343651", "title": "Data Scientist", "employer_name": "Kyndryl", "employer_slug": "kyndryl", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-09T00:00:00.000Z", "apply_url": "https://kyndryl.wd5.myworkdayjobs.com/KyndrylProfessionalCareers/job/Singapore-South-West-Singapore/Data-Scientist_R-52599-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Data Scientist 2 Locations posted: Posted 3 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.21178cf7b64ec5814e", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2ce24d6b2e69f533019c701ab2343651", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:2ce24d6b2e69f533019c701ab2343651:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1867072/000110465926067881/kd-20260331x10k.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://kyndryl.wd5.myworkdayjobs.com/KyndrylProfessionalCareers/job/Singapore-South-West-Singapore/Data-Scientist_R-52599-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://kyndryl.wd5.myworkdayjobs.com/KyndrylProfessionalCareers/job/Singapore-South-West-Singapore/Data-Scientist_R-52599-1", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "2e81315c43535580fb42f953b912d436", "title": "Member of Technical Staff - Infrastructure Engineer", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "Freiburg (Germany)", "country": "DE", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": 100000, "salary_max": 230000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "EUR", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 100000, "base_salary_max": 230000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2025-09-18T00:43:36.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4925659008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Member of Technical Staff - Infrastructure Engineer Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role We're looking for engineers to build and maintain the engine that powers our mission to develop visual intelligence. From maintaining and scaling clusters, to building research platforms to accelerate the rate of innovation, this team operates with large breadth and depth. We build the systems to make multi-week/month long training possible, to orchestrate resources at scale, and at the same time efficiently, enabling the next breakthrough model. If you're obsessed with distributed systems at scale, infrastructure reliability, scalability, security, and continuous improvement, this team would be perfect for you. What You'll Work On - Maintain research infrastructure, ensuring health, and optimizing components to extract peak performance from the system (both on application, and infrastructure side) - Scale infrastructure to meet growing research demands while maintaining reliability and performance - Collaborate with research teams to deeply understand their infrastructure needs, and design solutions that", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "staff_plus", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 8, "years_experience_max": 12, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": true, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.1f76a7cefe4d2f642c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2e81315c43535580fb42f953b912d436", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - Infrastructure Engineer Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role We're looking for engineers to build and maintain the engine that powers our mission to develop visual intelligence. From maintaining and scaling clusters, to building research platforms to accelerate the rate of innovation, this team operates with large breadth and depth. We build the systems to make multi-week/month long training possible, to orchestrate resources at scale, and at the same time efficiently, enabling the next breakthrough model. If you're obsessed with distributed systems at scale, infrastructure reliability, scalability, security, and continuous improvement, this team would be perfect for you. What You'll Work On - Maintain research infrastructure, ensuring health, and optimizing components to extract peak performance from the system (both on application, and infrastructure side) - Scale infrastructure to meet growing research demands while maintaining reliability and performance - Collaborate with research teams to deeply understand their infrastructure needs, and design solutions that", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:2e81315c43535580fb42f953b912d436:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.e7a5f37d010d419806", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2e81315c43535580fb42f953b912d436", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - Infrastructure Engineer Freiburg (Germany) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role We're looking for engineers to build and maintain the engine that powers our mission to develop visual intelligence. From maintaining and scaling clusters, to building research platforms to accelerate the rate of innovation, this team operates with large breadth and depth. We build the systems to make multi-week/month long training possible, to orchestrate resources at scale, and at the same time efficiently, enabling the next breakthrough model. If you're obsessed with distributed systems at scale, infrastructure reliability, scalability, security, and continuous improvement, this team would be perfect for you. What You'll Work On - Maintain research infrastructure, ensuring health, and optimizing components to extract peak performance from the system (both on application, and infrastructure side) - Scale infrastructure to meet growing research demands while maintaining reliability and performance - Collaborate with research teams to deeply understand their infrastructure needs, and design solutions that", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4925659008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4925659008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4925659008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4925659008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4925659008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4925659008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "2c3ae06a7218aec5abe0e3d66f19fac8", "title": "Consultant - Psychometric Assessments", "employer_name": "Marsh McLennan", "employer_slug": "marsh-mclennan", "location_text": "Gurugram - Good Earth", "country": "IN", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-08T00:00:00.000Z", "apply_url": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Gurugram---Good-Earth/Consultant---Psychometric-Assessments_R_315187", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Consultant - Psychometric Assessments Gurugram - Good Earth posted: Posted 4 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.f60271dd333b4a929e", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2c3ae06a7218aec5abe0e3d66f19fac8", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:2c3ae06a7218aec5abe0e3d66f19fac8:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Gurugram---Good-Earth/Consultant---Psychometric-Assessments_R_315187", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Gurugram---Good-Earth/Consultant---Psychometric-Assessments_R_315187", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "37c71f0d3a7b974d7d15b8d94ac87862", "title": "Forward Deployed Machine Learning Engineer", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "San Francisco (USA)", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 180000, "salary_max": 270000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 180000, "base_salary_max": 270000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2025-09-26T20:06:39.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Forward Deployed Machine Learning Engineer San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX - foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role You'll live at the intersection of cutting-edge research and brutal production reality. Your customers won't just want FLUX to work-they'll need it optimized for their specific hardware, fine-tuned for their unique use cases, and integrated into systems that weren't designed for diffusion models in the first place. What You'll Work On - Ensures FLUX models perform optimally in customer environments-whether that's on-premise GPU clusters or BFL-hosted infrastructure-balancing the eternal tension between latency and output quality - Architects deep product integrations that go far beyond \"here's an API endpoint\"-helping customers with everything from model hosting and deployment to inference optimization techniques that haven't made it into textbooks yet - Customizes our foundation models for visual media to solve problems customers couldn't articulate until you helped them understand what's possible - Sits in technical deep-dives with customers to diagnose performance bottlenecks, then translates those findings into solutions (and sometimes into research questions for our core team)", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.27521d904d5ca36cc6", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "37c71f0d3a7b974d7d15b8d94ac87862", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Forward Deployed Machine Learning Engineer San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX - foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role You'll live at the intersection of cutting-edge research and brutal production reality. Your customers won't just want FLUX to work-they'll need it optimized for their specific hardware, fine-tuned for their unique use cases, and integrated into systems that weren't designed for diffusion models in the first place. What You'll Work On - Ensures FLUX models perform optimally in customer environments-whether that's on-premise GPU clusters or BFL-hosted infrastructure-balancing the eternal tension between latency and output quality - Architects deep product integrations that go far beyond \"here's an API endpoint\"-helping customers with everything from model hosting and deployment to inference optimization techniques that haven't made it into textbooks yet - Customizes our foundation models for visual media to solve problems customers couldn't articulate until you helped them understand what's possible - Sits in technical deep-dives with customers to diagnose performance bottlenecks, then translates those findings into solutions (and sometimes into research questions for our core team)", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:37c71f0d3a7b974d7d15b8d94ac87862:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.f60b4c0302b51d1df6", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "37c71f0d3a7b974d7d15b8d94ac87862", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Forward Deployed Machine Learning Engineer San Francisco (USA) About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX - foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role You'll live at the intersection of cutting-edge research and brutal production reality. Your customers won't just want FLUX to work-they'll need it optimized for their specific hardware, fine-tuned for their unique use cases, and integrated into systems that weren't designed for diffusion models in the first place. What You'll Work On - Ensures FLUX models perform optimally in customer environments-whether that's on-premise GPU clusters or BFL-hosted infrastructure-balancing the eternal tension between latency and output quality - Architects deep product integrations that go far beyond \"here's an API endpoint\"-helping customers with everything from model hosting and deployment to inference optimization techniques that haven't made it into textbooks yet - Customizes our foundation models for visual media to solve problems customers couldn't articulate until you helped them understand what's possible - Sits in technical deep-dives with customers to diagnose performance bottlenecks, then translates those findings into solutions (and sometimes into research questions for our core team)", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/4935984008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "2e4d7eff67cd8b32462c3dfae915c6c1", "title": "(Senior) Aktuar / Mathematiker* (m/w/d)", "employer_name": "Marsh McLennan", "employer_slug": "marsh-mclennan", "location_text": "3 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-28T00:00:00.000Z", "apply_url": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Munich---Mllerstrasse/XMLNAME--Senior--Aktuar---Mathematiker---m-w-d-_R_338526", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "(Senior) Aktuar / Mathematiker* (m/w/d) 3 Locations posted: Posted 15 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.08294e5b89c88bda81", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "2e4d7eff67cd8b32462c3dfae915c6c1", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:2e4d7eff67cd8b32462c3dfae915c6c1:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/62709/000006270926000022/mrsh-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://mmc.wd1.myworkdayjobs.com/MMC/job/Munich---Mllerstrasse/XMLNAME--Senior--Aktuar---Mathematiker---m-w-d-_R_338526", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "399840670d3bf58b1b6a03f7c6477d46", "title": "Commercial Counsel", "employer_name": "Black Forest Labs", "employer_slug": "black-forest-labs", "location_text": "Freiburg im Breisgau, Baden-Württemberg, Germany, San Francisco, California, United States", "country": "unknown", "employment_type": "contract", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": 210000, "salary_max": 275000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 210000, "base_salary_max": 275000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2026-05-12T10:08:46.000Z", "apply_url": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Commercial Counsel Freiburg im Breisgau, Baden-Württemberg, Germany, San Francisco, California, United States About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role We are looking for a sharp, motivated, and commercially minded Commercial Counsel to join BFL's legal team. You will support a broad range of commercial transactions spanning our US and EU go-to-market deals, with the majority of deal volume in the United States. This is a high-ownership role in which you will draft and review agreements, lead negotiations, and serve as a trusted advisor to the business on all things commercial. This is an exciting opportunity for a lawyer who wants to work at the cutting edge of AI - advising on novel agreement structures for foundational generative AI models, negotiating complex licensing and API deals, and helping build the legal and commercial infrastructure of a high-growth company. What You'll Work On - Draft and negotiate a wide range of commercial agreements, including API licensing, enterprise SaaS, model access and", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 7, "years_experience_max": 7, "role_function": "legal", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.4d2e7e4d6030e4ade4", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "399840670d3bf58b1b6a03f7c6477d46", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Commercial Counsel Freiburg im Breisgau, Baden-Württemberg, Germany, San Francisco, California, United States About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role We are looking for a sharp, motivated, and commercially minded Commercial Counsel to join BFL's legal team. You will support a broad range of commercial transactions spanning our US and EU go-to-market deals, with the majority of deal volume in the United States. This is a high-ownership role in which you will draft and review agreements, lead negotiations, and serve as a trusted advisor to the business on all things commercial. This is an exciting opportunity for a lawyer who wants to work at the cutting edge of AI - advising on novel agreement structures for foundational generative AI models, negotiating complex licensing and API deals, and helping build the legal and commercial infrastructure of a high-growth company. What You'll Work On - Draft and negotiate a wide range of commercial agreements, including API licensing, enterprise SaaS, model access and", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:399840670d3bf58b1b6a03f7c6477d46:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.a23497cff027bb60b1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "399840670d3bf58b1b6a03f7c6477d46", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Commercial Counsel Freiburg im Breisgau, Baden-Württemberg, Germany, San Francisco, California, United States About Black Forest Labs We're the team behind Latent Diffusion, Stable Diffusion, and FLUX-foundational technologies that changed how the world creates images and video. We're creating the generative models that power how people make images and video-tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. Why This Role We are looking for a sharp, motivated, and commercially minded Commercial Counsel to join BFL's legal team. You will support a broad range of commercial transactions spanning our US and EU go-to-market deals, with the majority of deal volume in the United States. This is a high-ownership role in which you will draft and review agreements, lead negotiations, and serve as a trusted advisor to the business on all things commercial. This is an exciting opportunity for a lawyer who wants to work at the cutting edge of AI - advising on novel agreement structures for foundational generative AI models, negotiating complex licensing and API deals, and helping build the legal and commercial infrastructure of a high-growth company. What You'll Work On - Draft and negotiate a wide range of commercial agreements, including API licensing, enterprise SaaS, model access and", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/blackforestlabs/jobs/5217028008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "d7f4bf40fa78ee2ab1b4b3eb90cc578a", "title": "Senior Data Scientist", "employer_name": "Adobe Inc.", "employer_slug": "adobe", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Senior-Data-Scientist_R165355", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Data Scientist 2 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": 16, "non_birth_parent_leave_weeks": 16, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://benefits.adobe.com/us/time-off/leaves-of-absence", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 90, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://benefits.adobe.com/us/time-off/leaves-of-absence", "db_column": "parental_leave_weeks", "source_url": "https://benefits.adobe.com/us/time-off/leaves-of-absence" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://benefits.adobe.com/us/time-off/leaves-of-absence", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://benefits.adobe.com/us/time-off/leaves-of-absence" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Technology", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.77010f3e77ec69d3d1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "d7f4bf40fa78ee2ab1b4b3eb90cc578a", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:d7f4bf40fa78ee2ab1b4b3eb90cc578a:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/796343/000079634326000003/adbe-20251128.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Senior-Data-Scientist_R165355", "source_values": [ "https://benefits.adobe.com/us/time-off/leaves-of-absence" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Senior-Data-Scientist_R165355", "source_values": [ "https://benefits.adobe.com/us/time-off/leaves-of-absence" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Senior-Data-Scientist_R165355", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Senior-Data-Scientist_R165355", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "3b6e2e7f299b843ea394ba6d96eb1f76", "title": "HPC Data Center Infrastructure Planning Lead", "employer_name": "Jump Trading", "employer_slug": "jump-trading", "location_text": "Chicago, New York", "country": "US", "employment_type": "contract", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": 150000, "salary_max": 200000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 150000, "base_salary_max": 200000, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-04-14T19:08:01.000Z", "apply_url": "https://www.jumptrading.com/hr/job?gh_jid=7781551", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "HPC Data Center Infrastructure Planning Lead Chicago, New York HPC Infrastructure Planner Lead Location: Chicago or New York (On-site 5 days/week) Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. We are looking for an experienced HPC Infrastructure Planner Lead to oversee the full portfolio of infrastructure projects across our high-performance computing environment. In this role, you will own project prioritization and reporting, manage contractor relationships and schedules, and support new data center lease efforts. While this is a leadership and planning-focused position, you must also be able to step in and directly plan and drive projects when needed. If you", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.823a4544b7fd6e887c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "3b6e2e7f299b843ea394ba6d96eb1f76", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "HPC Data Center Infrastructure Planning Lead Chicago, New York HPC Infrastructure Planner Lead Location: Chicago or New York (On-site 5 days/week) Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. We are looking for an experienced HPC Infrastructure Planner Lead to oversee the full portfolio of infrastructure projects across our high-performance computing environment. In this role, you will own project prioritization and reporting, manage contractor relationships and schedules, and support new data center lease efforts. While this is a leadership and planning-focused position, you must also be able to step in and directly plan and drive projects when needed. If you", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:3b6e2e7f299b843ea394ba6d96eb1f76:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.ec67fd828d4a78e0f0", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "3b6e2e7f299b843ea394ba6d96eb1f76", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "HPC Data Center Infrastructure Planning Lead Chicago, New York HPC Infrastructure Planner Lead Location: Chicago or New York (On-site 5 days/week) Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of Engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. We are looking for an experienced HPC Infrastructure Planner Lead to oversee the full portfolio of infrastructure projects across our high-performance computing environment. In this role, you will own project prioritization and reporting, manage contractor relationships and schedules, and support new data center lease efforts. While this is a leadership and planning-focused position, you must also be able to step in and directly plan and drive projects when needed. If you", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781551", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781551", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781551", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781551", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781551", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=7781551", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "4837def32896686de9aa19decea9a9bc", "title": "Senior Statistician", "employer_name": "Abbott Laboratories", "employer_slug": "abbott-laboratories", "location_text": "United States - California - Alameda", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://abbott.wd5.myworkdayjobs.com/abbottcareers/job/United-States---California---Alameda/Senior-Statistician_31139400-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Statistician United States - California - Alameda posted: Posted 30+ Days Ago", "parental_leave_weeks": 8, "non_birth_parent_leave_weeks": 8, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.abbott.com/en-us/careers/benefits", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 81, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1800/000162828026010185/abt-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1800/000162828026010185/abt-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.abbott.com/en-us/careers/benefits", "db_column": "parental_leave_weeks", "source_url": "https://www.abbott.com/en-us/careers/benefits" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://www.abbott.com/en-us/careers/benefits", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://www.abbott.com/en-us/careers/benefits" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Technology", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.04f2767bb97e4d4c14", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4837def32896686de9aa19decea9a9bc", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:4837def32896686de9aa19decea9a9bc:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1800/000162828026010185/abt-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1800/000162828026010185/abt-20251231.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://abbott.wd5.myworkdayjobs.com/abbottcareers/job/United-States---California---Alameda/Senior-Statistician_31139400-1", "source_values": [ "https://www.abbott.com/en-us/careers/benefits" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://abbott.wd5.myworkdayjobs.com/abbottcareers/job/United-States---California---Alameda/Senior-Statistician_31139400-1", "source_values": [ "https://www.abbott.com/en-us/careers/benefits" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://abbott.wd5.myworkdayjobs.com/abbottcareers/job/United-States---California---Alameda/Senior-Statistician_31139400-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "4af13d997185308926d1eb6868189a0f", "title": "Research Engineer, World Models", "employer_name": "Waabi", "employer_slug": "waabi", "location_text": "Toronto, ON", "country": "CA", "employment_type": "full_time", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 155000, "salary_max": 269000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 155000, "base_salary_max": 269000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cnbc_disruptor50" ], "posted_at": "2026-04-02T15:36:53.842Z", "apply_url": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "apply_url_verified": false, "ats": "lever", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Research Engineer, World Models Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai Waabi's autonomous-driving stack is powered by Waabi World, which delivers realistic, scalable, controllable, and efficient simulation. As a Research Engineer in the World Models team, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World's ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training. You will… - Design, implement, and scale state-of-the-art generative and predictive world-modeling systems: Video generation and prediction Latent diffusion / autoregressive / flow-matching models multimodal foundation models for driving scenes LLM / VLM / VLA methods for scene understanding, reasoning, and control Generative scenario modeling and controllable simulation Model distillation - Collaborate closely with Research Scientists to translate cutting-edge", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": true, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "unlimited_pto": { "field": "unlimited_pto", "source": "llm:greenfield:inv_371_postfix", "db_column": "unlimited_pto" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "masters", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.5aab916c2a56d47500", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4af13d997185308926d1eb6868189a0f", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Research Engineer, World Models Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai Waabi's autonomous-driving stack is powered by Waabi World, which delivers realistic, scalable, controllable, and efficient simulation. As a Research Engineer in the World Models team, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World's ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training. You will… - Design, implement, and scale state-of-the-art generative and predictive world-modeling systems: Video generation and prediction Latent diffusion / autoregressive / flow-matching models multimodal foundation models for driving scenes LLM / VLM / VLA methods for scene understanding, reasoning, and control Generative scenario modeling and controllable simulation Model distillation - Collaborate closely with Research Scientists to translate cutting-edge", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:4af13d997185308926d1eb6868189a0f:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.4b4f713b33cec383e1", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4af13d997185308926d1eb6868189a0f", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Research Engineer, World Models Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai Waabi's autonomous-driving stack is powered by Waabi World, which delivers realistic, scalable, controllable, and efficient simulation. As a Research Engineer in the World Models team, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World's ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training. You will… - Design, implement, and scale state-of-the-art generative and predictive world-modeling systems: Video generation and prediction Latent diffusion / autoregressive / flow-matching models multimodal foundation models for driving scenes LLM / VLM / VLA methods for scene understanding, reasoning, and control Generative scenario modeling and controllable simulation Model distillation - Collaborate closely with Research Scientists to translate cutting-edge", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "unlimited_pto": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/ef8b9a24-a010-4ef6-814c-3503e268e9a3", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "d5df879f003ce7aa0d5ce710f13cd5c2", "title": "Statistical Programmer 2", "employer_name": "IQVIA", "employer_slug": "iqvia", "location_text": "3 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "profit_share" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://iqvia.wd1.myworkdayjobs.com/IQVIA/job/Bloemfontein-South-Africa/Statistical-Programmer-2_R1542703", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Statistical Programmer 2 3 Locations posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Healthcare", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.eb0b8826521d0a9e42", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "d5df879f003ce7aa0d5ce710f13cd5c2", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:d5df879f003ce7aa0d5ce710f13cd5c2:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://iqvia.wd1.myworkdayjobs.com/IQVIA/job/Bloemfontein-South-Africa/Statistical-Programmer-2_R1542703", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "4c25a1eb305d0e911ac9e1386eab9340", "title": "Compute Platform System Safety Engineer", "employer_name": "Zoox", "employer_slug": "zoox", "location_text": "Foster City, CA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base_plus_commission", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-01-02T19:27:44.864Z", "apply_url": "https://jobs.lever.co/zoox/7ca52fd4-a683-4ba9-a635-5155f90be4f7", "apply_url_verified": false, "ats": "lever", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Compute Platform System Safety Engineer Foster City, CA Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and deploy such a robotaxi solution safely and reliably. We are seeking a System Safety Engineer to perform safety analyses of complex electronic and software-based-systems, specifically high performance compute. The ideal candidate will be on the Zoox's System Design and Mission Assurance (SDMA) team to identify opportunities to improve, adapt, and generally refine our safety concept, safety mechanisms to detect and mitigate hardware failures, and residual risk assessments, ensuring the safe deployment of autonomous driving technology. Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and deploy such a robotaxi solution safely and reliably. We are seeking a System Safety Engineer to perform safety analyses of complex electronic and software-based-systems, specifically high performance compute. The ideal candidate will be on the Zoox's System Design and Mission Assurance (SDMA) team to identify opportunities to improve, adapt, and generally refine our safety concept, safety mechanisms to detect and mitigate hardware failures, and residual risk assessments, ensuring the safe deployment of autonomous driving technology. In this role, you will: - Execute and own the output of the Compute Safety Analysis (FMEDA) for various compute platforms for each milestone - Respond to and aid in Root Cause Analysis (RCA) for safety-related Compute Platform field failures - Collaborate with partner teams to improve diagnostic coverage against single point and multiple point latent failures on our existing", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "equity_type": { "field": "equity_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_type" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.decd51e06d92982693", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4c25a1eb305d0e911ac9e1386eab9340", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Compute Platform System Safety Engineer Foster City, CA Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and deploy such a robotaxi solution safely and reliably. We are seeking a System Safety Engineer to perform safety analyses of complex electronic and software-based-systems, specifically high performance compute. The ideal candidate will be on the Zoox's System Design and Mission Assurance (SDMA) team to identify opportunities to improve, adapt, and generally refine our safety concept, safety mechanisms to detect and mitigate hardware failures, and residual risk assessments, ensuring the safe deployment of autonomous driving technology. Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and deploy such a robotaxi solution safely and reliably. We are seeking a System Safety Engineer to perform safety analyses of complex electronic and software-based-systems, specifically high performance compute. The ideal candidate will be on the Zoox's System Design and Mission Assurance (SDMA) team to identify opportunities to improve, adapt, and generally refine our safety concept, safety mechanisms to detect and mitigate hardware failures, and residual risk assessments, ensuring the safe deployment of autonomous driving technology. In this role, you will: - Execute and own the output of the Compute Safety Analysis (FMEDA) for various compute platforms for each milestone - Respond to and aid in Root Cause Analysis (RCA) for safety-related Compute Platform field failures - Collaborate with partner teams to improve diagnostic coverage against single point and multiple point latent failures on our existing", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:4c25a1eb305d0e911ac9e1386eab9340:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.6f4debc1a430ce7717", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "4c25a1eb305d0e911ac9e1386eab9340", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Compute Platform System Safety Engineer Foster City, CA Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and deploy such a robotaxi solution safely and reliably. We are seeking a System Safety Engineer to perform safety analyses of complex electronic and software-based-systems, specifically high performance compute. The ideal candidate will be on the Zoox's System Design and Mission Assurance (SDMA) team to identify opportunities to improve, adapt, and generally refine our safety concept, safety mechanisms to detect and mitigate hardware failures, and residual risk assessments, ensuring the safe deployment of autonomous driving technology. Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and deploy such a robotaxi solution safely and reliably. We are seeking a System Safety Engineer to perform safety analyses of complex electronic and software-based-systems, specifically high performance compute. The ideal candidate will be on the Zoox's System Design and Mission Assurance (SDMA) team to identify opportunities to improve, adapt, and generally refine our safety concept, safety mechanisms to detect and mitigate hardware failures, and residual risk assessments, ensuring the safe deployment of autonomous driving technology. In this role, you will: - Execute and own the output of the Compute Safety Analysis (FMEDA) for various compute platforms for each milestone - Respond to and aid in Root Cause Analysis (RCA) for safety-related Compute Platform field failures - Collaborate with partner teams to improve diagnostic coverage against single point and multiple point latent failures on our existing", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/zoox/7ca52fd4-a683-4ba9-a635-5155f90be4f7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/zoox/7ca52fd4-a683-4ba9-a635-5155f90be4f7", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/zoox/7ca52fd4-a683-4ba9-a635-5155f90be4f7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/zoox/7ca52fd4-a683-4ba9-a635-5155f90be4f7", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "f66eb2d6cab0ac3abd9a2d72fec0623d", "title": "Senior Precision Modeling Engineer", "employer_name": "Analog Devices", "employer_slug": "analog-devices", "location_text": "Ireland, Limerick", "country": "IE", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp", "profit_share" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://analogdevices.wd1.myworkdayjobs.com/External/job/Ireland-Limerick/Senior-Precision-Modeling-Engineer_R261339", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Precision Modeling Engineer Ireland, Limerick posted: Posted 30+ Days Ago", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 77, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf", "db_column": "parental_leave_weeks", "source_url": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Technology", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.758a21e1fbe972312d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "f66eb2d6cab0ac3abd9a2d72fec0623d", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:f66eb2d6cab0ac3abd9a2d72fec0623d:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/6281/000000628125000153/adi-20251101.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/Ireland-Limerick/Senior-Precision-Modeling-Engineer_R261339", "source_values": [ "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/Ireland-Limerick/Senior-Precision-Modeling-Engineer_R261339", "source_values": [ "https://www.analog.com/media/en/company-csr/benefits-at-a-glance.pdf" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/Ireland-Limerick/Senior-Precision-Modeling-Engineer_R261339", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://analogdevices.wd1.myworkdayjobs.com/External/job/Ireland-Limerick/Senior-Precision-Modeling-Engineer_R261339", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "57368d7332ac8327e964d85e3b7f1462", "title": "Research Scientist, World Models", "employer_name": "Waabi", "employer_slug": "waabi", "location_text": "Toronto, ON", "country": "CA", "employment_type": "full_time", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 155000, "salary_max": 269000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 155000, "base_salary_max": 269000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cnbc_disruptor50" ], "posted_at": "2026-03-27T16:23:00.513Z", "apply_url": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "apply_url_verified": false, "ats": "lever", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Research Scientist, World Models Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai Waabi's autonomous-driving stack is powered by Waabi World, which aims to deliver realistic, scalable, controllable, and efficient simulation. As a Research Scientist in World Models, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World's ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training. You will… - Conduct fundamental and applied research in generative and predictive world-modeling • Video generation and prediction. • Latent diffusion / autoregressive / flow-matching models. • Multimodal foundation models for driving scenes. • LLM / VLM / VLA methods for scene understanding, reasoning, and control. • Generative scenario modeling and controllable simulation. • Model distillation. - Collaborate with", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": true, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "unlimited_pto": { "field": "unlimited_pto", "source": "llm:greenfield:inv_371_postfix", "db_column": "unlimited_pto" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.73a0a7563dcfb8e5ad", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "57368d7332ac8327e964d85e3b7f1462", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Research Scientist, World Models Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai Waabi's autonomous-driving stack is powered by Waabi World, which aims to deliver realistic, scalable, controllable, and efficient simulation. As a Research Scientist in World Models, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World's ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training. You will… - Conduct fundamental and applied research in generative and predictive world-modeling • Video generation and prediction. • Latent diffusion / autoregressive / flow-matching models. • Multimodal foundation models for driving scenes. • LLM / VLM / VLA methods for scene understanding, reasoning, and control. • Generative scenario modeling and controllable simulation. • Model distillation. - Collaborate with", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:57368d7332ac8327e964d85e3b7f1462:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.f978133966e975f97b", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "57368d7332ac8327e964d85e3b7f1462", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Research Scientist, World Models Toronto, ON Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech. With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai Waabi's autonomous-driving stack is powered by Waabi World, which aims to deliver realistic, scalable, controllable, and efficient simulation. As a Research Scientist in World Models, you will develop algorithms and productionize the next generation of World Models that can reason about complex, dynamic 4D environments.This role focuses on large-scale world models for temporal reasoning and generation, including video models, multimodal generative models, LLM/VLM/VLA models, and predictive models of traffic participants and scenes. Your work will directly power Waabi World's ability to model future evolution, synthesize realistic safety-critical scenarios, and provide rich generative priors for downstream planning, testing, and training. You will… - Conduct fundamental and applied research in generative and predictive world-modeling • Video generation and prediction. • Latent diffusion / autoregressive / flow-matching models. • Multimodal foundation models for driving scenes. • LLM / VLM / VLA methods for scene understanding, reasoning, and control. • Generative scenario modeling and controllable simulation. • Model distillation. - Collaborate with", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "unlimited_pto": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.lever.co/waabi/37af7fe8-1d7f-458d-9e8a-942ac0c75a3d", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "0f92348b82570ffa5ff1bbebfd8184ac", "title": "Senior Data Scientist", "employer_name": "Equifax", "employer_slug": "equifax", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-01T00:00:00.000Z", "apply_url": "https://equifax.wd5.myworkdayjobs.com/External/job/USA---Atlanta---One-Atlantic-Center/Senior-Data-Scientist_J00177373", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Data Scientist 2 Locations posted: Posted 11 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.d61a8933dada19a3ac", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0f92348b82570ffa5ff1bbebfd8184ac", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:0f92348b82570ffa5ff1bbebfd8184ac:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/33185/000003318526000010/efx-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://equifax.wd5.myworkdayjobs.com/External/job/USA---Atlanta---One-Atlantic-Center/Senior-Data-Scientist_J00177373", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://equifax.wd5.myworkdayjobs.com/External/job/USA---Atlanta---One-Atlantic-Center/Senior-Data-Scientist_J00177373", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "81e5b09d1878c7212201ff833a329704", "title": "Senior Data Scientist - GTM Data", "employer_name": "PandaDoc", "employer_slug": "pandadoc", "location_text": "Remote (USA)", "country": "US", "employment_type": "unknown", "remote_status": "remote", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 0, "timezone_overlap_hours": 6, "salary_min": 165000, "salary_max": 185000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 165000, "base_salary_max": 185000, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn" ], "posted_at": "2026-05-11T14:06:42.000Z", "apply_url": "https://job-boards.greenhouse.io/pandadoc/jobs/7906530", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Data Scientist - GTM Data Remote (USA) The Opportunity As a Senior GTM Data Scientist at PandaDoc, you will be a critical analytical partner to our Go-To-Market (GTM) teams. You will embed yourself in our GTM data to uncover insights and drive actionable recommendations across Sales, Marketing, and Customer Success. The core of this role is to design, build, and maintain predictive machine learning models that optimize customer acquisition, revenue attribution, and retention efforts. You will apply analytical rigor and methodologies like experimentation and causal inference to provide GTM leadership with a reliable understanding of business efficiency and impact. You will report to the Director of GTM Data and act as a reliable thought partner to Marketing, Sales, Customer Success, and Finance. What You'll Do Predictive Modeling & GTM Strategy - Model Development: Design, build, and deploy foundational GTM models, including Customer Lifetime Value (LTV) forecasting, Marketing and Sales Attribution, and Propensity models (e.g., propensity to convert, churn, or expand). - GTM Experimentation: Partner with GTM teams to design and analyze controlled experiments across various channels, including website A/B testing, pricing experiments, and marketing campaign effectiveness. You will use methodologies such as AB, multivariate, Bayesian, and Causal Inference. - Deep Dive Analysis: Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations. - Marketing Mix Modeling (MMM): Support the interpretation of MMM results to help maximize marketing ROI and assess the feasibility of future in-house", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 55, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 4, "years_experience_max": 9, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "masters", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.5636c17df2dc925964", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "81e5b09d1878c7212201ff833a329704", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior Data Scientist - GTM Data Remote (USA) The Opportunity As a Senior GTM Data Scientist at PandaDoc, you will be a critical analytical partner to our Go-To-Market (GTM) teams. You will embed yourself in our GTM data to uncover insights and drive actionable recommendations across Sales, Marketing, and Customer Success. The core of this role is to design, build, and maintain predictive machine learning models that optimize customer acquisition, revenue attribution, and retention efforts. You will apply analytical rigor and methodologies like experimentation and causal inference to provide GTM leadership with a reliable understanding of business efficiency and impact. You will report to the Director of GTM Data and act as a reliable thought partner to Marketing, Sales, Customer Success, and Finance. What You'll Do Predictive Modeling & GTM Strategy - Model Development: Design, build, and deploy foundational GTM models, including Customer Lifetime Value (LTV) forecasting, Marketing and Sales Attribution, and Propensity models (e.g., propensity to convert, churn, or expand). - GTM Experimentation: Partner with GTM teams to design and analyze controlled experiments across various channels, including website A/B testing, pricing experiments, and marketing campaign effectiveness. You will use methodologies such as AB, multivariate, Bayesian, and Causal Inference. - Deep Dive Analysis: Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations. - Marketing Mix Modeling (MMM): Support the interpretation of MMM results to help maximize marketing ROI and assess the feasibility of future in-house", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:81e5b09d1878c7212201ff833a329704:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.abb9564debb4ad4e10", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "81e5b09d1878c7212201ff833a329704", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Senior Data Scientist - GTM Data Remote (USA) The Opportunity As a Senior GTM Data Scientist at PandaDoc, you will be a critical analytical partner to our Go-To-Market (GTM) teams. You will embed yourself in our GTM data to uncover insights and drive actionable recommendations across Sales, Marketing, and Customer Success. The core of this role is to design, build, and maintain predictive machine learning models that optimize customer acquisition, revenue attribution, and retention efforts. You will apply analytical rigor and methodologies like experimentation and causal inference to provide GTM leadership with a reliable understanding of business efficiency and impact. You will report to the Director of GTM Data and act as a reliable thought partner to Marketing, Sales, Customer Success, and Finance. What You'll Do Predictive Modeling & GTM Strategy - Model Development: Design, build, and deploy foundational GTM models, including Customer Lifetime Value (LTV) forecasting, Marketing and Sales Attribution, and Propensity models (e.g., propensity to convert, churn, or expand). - GTM Experimentation: Partner with GTM teams to design and analyze controlled experiments across various channels, including website A/B testing, pricing experiments, and marketing campaign effectiveness. You will use methodologies such as AB, multivariate, Bayesian, and Causal Inference. - Deep Dive Analysis: Execute proactive, complex analytical deep dives to discover latent user behavior and root causes of changes in GTM metrics, translating findings into actionable recommendations. - Marketing Mix Modeling (MMM): Support the interpretation of MMM results to help maximize marketing ROI and assess the feasibility of future in-house", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7906530", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7906530", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7906530", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7906530", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7906530", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7906530", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "0fd4b28fa6574088b13da0c33a7d5e23", "title": "Estimateur sénior", "employer_name": "Trane Technologies plc", "employer_slug": "trane-technologies", "location_text": "Quebec, Quebec", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://tranetechnologies.wd12.myworkdayjobs.com/Trane_Technologies_Careers/job/Quebec-Quebec/Estimateur-snior_92507383-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Estimateur sénior Quebec, Quebec posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1466258/000162828026005731/tt-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1466258/000162828026005731/tt-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.6fbdb2626efff89b1e", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "0fd4b28fa6574088b13da0c33a7d5e23", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:0fd4b28fa6574088b13da0c33a7d5e23:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1466258/000162828026005731/tt-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1466258/000162828026005731/tt-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://tranetechnologies.wd12.myworkdayjobs.com/Trane_Technologies_Careers/job/Quebec-Quebec/Estimateur-snior_92507383-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "83f242eca4af0e4255d32e27567f3bb5", "title": "Software Engineer (Frontend)", "employer_name": "Latent", "employer_slug": "latent", "location_text": "San Francisco | OnSite", "country": "US", "employment_type": "unknown", "remote_status": "onsite", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 5, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "yc" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/latent/c54c96d7-2776-41be-b74c-b2beaad99634", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Engineer (Frontend) San Francisco | OnSite FRONTEND ENGINEER Location: San Francisco, CA We are building the next generation of AI-enabled clinical systems, moving beyond the archaic Electronic Health Record (EHR) experience. Your designs will help providers and patients access critical data intuitively and efficiently, directly contributing to faster coverage, expedited treatment, and improved patient care. WHAT WE LOOK FOR IN A GREAT ENGINEER You are excited to translate massive backend and AI complexity into cogent, high-leverage user experiences. You thrive in our high-energy, in-office culture, and are eager to master the deep technical and healthcare domains required to build exceptional solutions. - High Standards: You hold yourself and others to high standards for code quality and user experience. - Domain Curiosity: You love learning new technologies and the complex clinical domains we operate in (e.g., Prior Authorizations). - Bias for Action: You are willing to jump into whatever needs to get done and won't wait for someone else to solve a problem that you're in a position to solve. - Tool Proficiency: You are highly proficient with your tools, speaking command line fluently and relying on keyboard shortcuts. WHAT YOU'LL WORK ON (RESPONSIBILITIES) As a Frontend Engineer, you will own the user interface and frontend architecture for our core product: - UX Architecture: Architect a flexible and capable React ecosystem designed to host complex, mission-critical healthcare widgets and user-facing AI tools. - Intuitive Design: Focus on high-level UI/UX to help providers and operational staff effortlessly navigate clinical data and", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "growth-stage", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "masters", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.950bd7e5f6366a5427", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "83f242eca4af0e4255d32e27567f3bb5", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:83f242eca4af0e4255d32e27567f3bb5:employer:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.employer_name.64c1d65cb7c431104c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "83f242eca4af0e4255d32e27567f3bb5", "signal_type": "query_match", "display_text": "Employer: \"latent\"", "tooltip": "Employer contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "employer_name", "source_value": "Latent", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c54c96d7-2776-41be-b74c-b2beaad99634", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c54c96d7-2776-41be-b74c-b2beaad99634", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c54c96d7-2776-41be-b74c-b2beaad99634", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/latent/c54c96d7-2776-41be-b74c-b2beaad99634", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "86103a3e6b675cde796c64b074fc1470", "title": "Senior AI Machine Learning Scientist", "employer_name": "Elevance Health, Inc.", "employer_slug": "elevance-health", "location_text": "3 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T00:00:00.000Z", "apply_url": "https://elevancehealth.wd1.myworkdayjobs.com/ANT/job/VA-RICHMOND-2015-STAPLES-MILL-RD/Senior-AI-Machine-Learning-Scientist_JR191033", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior AI Machine Learning Scientist 3 Locations posted: Posted Yesterday", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1156039/000115603926000013/elv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1156039/000115603926000013/elv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Healthcare", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.461076c53d3c9a8112", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "86103a3e6b675cde796c64b074fc1470", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:86103a3e6b675cde796c64b074fc1470:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1156039/000115603926000013/elv-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1156039/000115603926000013/elv-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://elevancehealth.wd1.myworkdayjobs.com/ANT/job/VA-RICHMOND-2015-STAPLES-MILL-RD/Senior-AI-Machine-Learning-Scientist_JR191033", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://elevancehealth.wd1.myworkdayjobs.com/ANT/job/VA-RICHMOND-2015-STAPLES-MILL-RD/Senior-AI-Machine-Learning-Scientist_JR191033", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "89fa21abfb4c7f2cba9dbab27de9459b", "title": "Staff GTM Data Scientist", "employer_name": "PandaDoc", "employer_slug": "pandadoc", "location_text": "Remote (USA)", "country": "US", "employment_type": "unknown", "remote_status": "remote", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 0, "timezone_overlap_hours": 6, "salary_min": 190000, "salary_max": 210000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 190000, "base_salary_max": 210000, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn" ], "posted_at": "2025-12-18T13:08:36.000Z", "apply_url": "https://job-boards.greenhouse.io/pandadoc/jobs/7483765", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Staff GTM Data Scientist Remote (USA) The Opportunity As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality. You will report to the Director of GTM Data and act as a strategic thought partner to Go-to-Market teams, Marketing, Product, Finance, Design, Engineering, and executive leadership, ensuring alignment between data insights and critical business decisions. What You'll Do Experimentation & Causal Strategy - Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). - Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. - Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. - Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends,", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 55, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "staff_plus", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 6, "years_experience_max": 7, "role_function": "data", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "masters", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.f2737b14c49efb5435", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "89fa21abfb4c7f2cba9dbab27de9459b", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Staff GTM Data Scientist Remote (USA) The Opportunity As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality. You will report to the Director of GTM Data and act as a strategic thought partner to Go-to-Market teams, Marketing, Product, Finance, Design, Engineering, and executive leadership, ensuring alignment between data insights and critical business decisions. What You'll Do Experimentation & Causal Strategy - Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). - Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. - Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. - Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends,", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:89fa21abfb4c7f2cba9dbab27de9459b:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.b93732d847a0b1af40", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "89fa21abfb4c7f2cba9dbab27de9459b", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Staff GTM Data Scientist Remote (USA) The Opportunity As a Staff Data Scientist at PandaDoc, you will serve as a senior analytical leader, embedding yourself deeply in our product and business data to uncover non-obvious insights and drive actionable recommendations. A primary focus of this strategic role is to champion and drive the organizational shift toward a data-driven culture. You will own the advancement of our experimentation capabilities, train other analysts and data scientists on causal methodologies, and leverage your expertise to provide leadership with a clear, reliable understanding of true impact and causality. You will report to the Director of GTM Data and act as a strategic thought partner to Go-to-Market teams, Marketing, Product, Finance, Design, Engineering, and executive leadership, ensuring alignment between data insights and critical business decisions. What You'll Do Experimentation & Causal Strategy - Lead the Experimentation Roadmap: Define, champion, and execute a strategic roadmap for measuring impact across PandaDoc, focusing on high-leverage business questions related to customer workflows, churn risk, and long-term value (LTV). - Advanced Experiment Design: Design, implement, and rigorously analyze complex A/B tests, multivariate experiments, and adaptive experimentation methods, including the application of Bayesian experimentation, to assess the effectiveness of proposed product changes and business levers. - Causal Inference Beyond A/B: Apply advanced causal inference techniques (e.g., difference-in-differences, synthetic control, propensity score matching, and instrumental variables) to scenarios where randomized controlled trials (RCTs) are infeasible. - Deep Dive Analysis: Conduct complex, proactive, and exploratory analysis to discover latent user behavior, emerging trends,", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7483765", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7483765", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7483765", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7483765", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7483765", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/pandadoc/jobs/7483765", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "8b89fc531be6d517bbe1cae5a8511934", "title": "Software Development Engineer, CreativeX", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "New York, New York, USA", "country": "US", "employment_type": "internship", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 158100, "salary_max": 213800, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 158100, "base_salary_max": 213800, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-13T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Development Engineer, CreativeX New York, New York, USA The CreativeX RAPID (Real-time Ad Personalization & Insights Development) team is seeking passionate and talented Machine Learning Engineer (MLE) to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - Quickly acquire knowledge of nouvelle technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain. - Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems. - Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models. - Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications. Basic Qualifications: - 3+ years of non-internship professional software development experience - 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience programming with at", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 56, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.8b5adb3f6cbcf741ce", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8b89fc531be6d517bbe1cae5a8511934", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Development Engineer, CreativeX New York, New York, USA The CreativeX RAPID (Real-time Ad Personalization & Insights Development) team is seeking passionate and talented Machine Learning Engineer (MLE) to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - Quickly acquire knowledge of nouvelle technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain. - Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems. - Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models. - Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications. Basic Qualifications: - 3+ years of non-internship professional software development experience - 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience programming with at", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:8b89fc531be6d517bbe1cae5a8511934:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.5c0c56d4584a855ee3", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "8b89fc531be6d517bbe1cae5a8511934", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Development Engineer, CreativeX New York, New York, USA The CreativeX RAPID (Real-time Ad Personalization & Insights Development) team is seeking passionate and talented Machine Learning Engineer (MLE) to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - Quickly acquire knowledge of nouvelle technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain. - Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems. - Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models. - Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications. Basic Qualifications: - 3+ years of non-internship professional software development experience - 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience programming with at", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "fertility_family_building_benefits": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "adoption_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "childcare_subsidy": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10418941/software-development-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "cabae4b572016a88e4a31415049bf605", "title": "Statistician", "employer_name": "IQVIA", "employer_slug": "iqvia", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "profit_share" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-03T00:00:00.000Z", "apply_url": "https://iqvia.wd1.myworkdayjobs.com/IQVIA/job/Beijing-China/Statistician_R1548668", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Statistician 2 Locations posted: Posted 9 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Healthcare", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.4a1b83a03a4a5e7d4e", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "cabae4b572016a88e4a31415049bf605", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:cabae4b572016a88e4a31415049bf605:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1478242/000162828026008322/iqv-20251231.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://iqvia.wd1.myworkdayjobs.com/IQVIA/job/Beijing-China/Statistician_R1548668", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "98cbf59b32d589045a9cddf89457b12f", "title": "Applied Scientist Gen AI - Amazon Advertising, CreativeX", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Seattle, Washington, USA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 136000, "salary_max": 184000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 136000, "base_salary_max": 184000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-11T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Applied Scientist Gen AI - Amazon Advertising, CreativeX Seattle, Washington, USA Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists and product managers, UX and engineers who are highly collaborative and at the top of their respective fields. We are looking for talented Applied Scientists who are adept at a variety of skills, especially at the Agentic AI, development and use of multi-modal Generative AI and can use state-of-the-art generative music and", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 56, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.e22181b7686580619d", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "98cbf59b32d589045a9cddf89457b12f", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Applied Scientist Gen AI - Amazon Advertising, CreativeX Seattle, Washington, USA Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists and product managers, UX and engineers who are highly collaborative and at the top of their respective fields. We are looking for talented Applied Scientists who are adept at a variety of skills, especially at the Agentic AI, development and use of multi-modal Generative AI and can use state-of-the-art generative music and", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:98cbf59b32d589045a9cddf89457b12f:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.ea9b6f697b12b9ec14", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "98cbf59b32d589045a9cddf89457b12f", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Applied Scientist Gen AI - Amazon Advertising, CreativeX Seattle, Washington, USA Amazon Advertising is one of Amazon's fastest growing businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X team within Amazon Advertising time aims to democratize access to high-quality creatives (audio, images, videos, text) by building AI-driven solutions for advertisers. To accomplish this, we are investing in understanding how best users can leverage Generative AI methods such as latent-diffusion models, large language models (LLM), generative audio (music and speech synthesis), computer vision (CV), reinforced learning (RL) and related. As an Applied Scientist you will be part of a close-knit team of other applied scientists and product managers, UX and engineers who are highly collaborative and at the top of their respective fields. We are looking for talented Applied Scientists who are adept at a variety of skills, especially at the Agentic AI, development and use of multi-modal Generative AI and can use state-of-the-art generative music and", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "fertility_family_building_benefits": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "mental_health_support": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "adoption_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "childcare_subsidy": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10416612/applied-scientist-gen-ai-amazon-advertising-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "1b36d3828b0ad2c87f458c3312cfaaf0", "title": "Consultant", "employer_name": "Neural Magic", "employer_slug": "neural-magic", "location_text": "Canberra", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "a16z" ], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://redhat.wd5.myworkdayjobs.com/jobs/job/Canberra/Consultant_R-054263-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Consultant Canberra posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.ea5c9db0032ad6d815", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "1b36d3828b0ad2c87f458c3312cfaaf0", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:1b36d3828b0ad2c87f458c3312cfaaf0:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://redhat.wd5.myworkdayjobs.com/jobs/job/Canberra/Consultant_R-054263-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://redhat.wd5.myworkdayjobs.com/jobs/job/Canberra/Consultant_R-054263-1", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "9c300a83307c0a73906b3c5160035cc1", "title": "Machine Learning Engineer, CreativeX", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "New York, New York, USA", "country": "US", "employment_type": "internship", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 158100, "salary_max": 213800, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 158100, "base_salary_max": 213800, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-15T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Engineer, CreativeX New York, New York, USA The CreativeX RAPID (Real-time Ad Personalization & Insights Development) team is seeking passionate and talented Machine Learning Engineer to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - Quickly acquire knowledge of nouvelle technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain. - Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems. - Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models. - Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications. Basic Qualifications: - 3+ years of non-internship professional software development experience - 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience programming with at least", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 56, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.005a0ba2d8f028feac", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "9c300a83307c0a73906b3c5160035cc1", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Machine Learning Engineer, CreativeX New York, New York, USA The CreativeX RAPID (Real-time Ad Personalization & Insights Development) team is seeking passionate and talented Machine Learning Engineer to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - Quickly acquire knowledge of nouvelle technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain. - Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems. - Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models. - Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications. Basic Qualifications: - 3+ years of non-internship professional software development experience - 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience programming with at least", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:9c300a83307c0a73906b3c5160035cc1:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.ef1f3a203995feb93f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "9c300a83307c0a73906b3c5160035cc1", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Machine Learning Engineer, CreativeX New York, New York, USA The CreativeX RAPID (Real-time Ad Personalization & Insights Development) team is seeking passionate and talented Machine Learning Engineer to join us. CreativeX is on a mission to enable brands of all sizes and categories to create, serve, measure, and optimize creative content with ease. Our team is responsible for tailoring the visual experience of ads to each context in real-time, leveraging nouvelle technologies such as latent diffusion models, large language models (LLM), reinforced learning (RL), computer vision, and related methods. Our mission is to provide engaging, dynamically optimized creatives with low latencies both on and off Amazon for self-service brand advertisers. We automate the customization of product creatives with real-time catalog data while providing advertisers with the flexibility to customize their creatives according to their preferences. Key job responsibilities - Quickly acquire knowledge of nouvelle technologies in Generative AI to solve complex problems in the Dynamic Creative Optimization (DCO) domain. - Investigate design approaches, prototype new technologies, and evaluate their technical feasibility, such as Auto ML, real-time ML serving systems. - Collaborate with scientists to design and build data pipelines for processing massive datasets and scaling machine learning models. - Develop and maintain platforms for developing, evaluating, and deploying machine learning models for real-world applications. Basic Qualifications: - 3+ years of non-internship professional software development experience - 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience - Experience programming with at least", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "source_values": [ "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" ], "checked_at": "2026-06-07T00:00:00Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10421033/machine-learning-engineer-creativex", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "ed0ffc10f8e1b106fa24bc391c1c9042", "title": "Senior Data Analyst", "employer_name": "Concentrix", "employer_slug": "concentrix", "location_text": "Omaha, NE 68179", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T00:00:00.000Z", "apply_url": "https://cnx.wd1.myworkdayjobs.com/external_global/job/Omaha-NE-68179/Senior-Data-Analyst_R1738483", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Data Analyst Omaha, NE 68179 posted: Posted Yesterday", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1803599/000180359926000027/cnxc-20251130.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1803599/000180359926000027/cnxc-20251130.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.a374048c140344b68f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "ed0ffc10f8e1b106fa24bc391c1c9042", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:ed0ffc10f8e1b106fa24bc391c1c9042:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1803599/000180359926000027/cnxc-20251130.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1803599/000180359926000027/cnxc-20251130.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://cnx.wd1.myworkdayjobs.com/external_global/job/Omaha-NE-68179/Senior-Data-Analyst_R1738483", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://cnx.wd1.myworkdayjobs.com/external_global/job/Omaha-NE-68179/Senior-Data-Analyst_R1738483", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "a3df9e7235631d1d94936e62c47c4314", "title": "Technical Program Manager III, AI/ML Data Analysis, Search", "employer_name": "Google", "employer_slug": "google", "location_text": "Mountain View, CA, USA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 163000, "salary_max": 237000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 163000, "base_salary_max": 237000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-05T16:09:32.000Z", "apply_url": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "apply_url_verified": false, "ats": "google_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Technical Program Manager III, AI/ML Data Analysis, Search Mountain View, CA, USA A problem isn't truly solved until it's solved for all. That's why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you'll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You'll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers. As the TPM of Human Measurement and Validation, you will be the chief architect of the human evaluation systems that power our Reinforcement Learning (RL) models in Search. Approaching AI evaluation as a massive-scale psychometric challenge, you will translate complex, latent constructs, such as model helpfulness, safety, and reasoning, into highly reliable, standardized behavioral assessments. Bridging the gap between ML research and the science of human measurement, you will design coding manuals, scale automated quality assurance pipelines, and lead statistical calibration. Your work will ensure our global RLHF data maximizes construct validity, minimizes measurement error, and delivers the clinical-grade accuracy required to train safe, capable AI. In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful", "parental_leave_weeks": 18, "non_birth_parent_leave_weeks": 18, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://blog.google/company-news/outreach-and-initiatives/diversity/international-womens-day-2022/", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 94, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1652044/000165204426000018/goog-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1652044/000165204426000018/goog-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://blog.google/company-news/outreach-and-initiatives/diversity/international-womens-day-2022/", "db_column": "parental_leave_weeks", "source_url": "https://blog.google/company-news/outreach-and-initiatives/diversity/international-womens-day-2022/" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://blog.google/company-news/outreach-and-initiatives/diversity/international-womens-day-2022/", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://blog.google/company-news/outreach-and-initiatives/diversity/international-womens-day-2022/" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "operations", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "Technology", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.0c7d79ba78473eb532", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a3df9e7235631d1d94936e62c47c4314", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Technical Program Manager III, AI/ML Data Analysis, Search Mountain View, CA, USA A problem isn't truly solved until it's solved for all. That's why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you'll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You'll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers. As the TPM of Human Measurement and Validation, you will be the chief architect of the human evaluation systems that power our Reinforcement Learning (RL) models in Search. Approaching AI evaluation as a massive-scale psychometric challenge, you will translate complex, latent constructs, such as model helpfulness, safety, and reasoning, into highly reliable, standardized behavioral assessments. Bridging the gap between ML research and the science of human measurement, you will design coding manuals, scale automated quality assurance pipelines, and lead statistical calibration. Your work will ensure our global RLHF data maximizes construct validity, minimizes measurement error, and delivers the clinical-grade accuracy required to train safe, capable AI. In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:a3df9e7235631d1d94936e62c47c4314:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.10318f882fe476d97c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "a3df9e7235631d1d94936e62c47c4314", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Technical Program Manager III, AI/ML Data Analysis, Search Mountain View, CA, USA A problem isn't truly solved until it's solved for all. That's why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you'll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You'll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers. As the TPM of Human Measurement and Validation, you will be the chief architect of the human evaluation systems that power our Reinforcement Learning (RL) models in Search. Approaching AI evaluation as a massive-scale psychometric challenge, you will translate complex, latent constructs, such as model helpfulness, safety, and reasoning, into highly reliable, standardized behavioral assessments. Bridging the gap between ML research and the science of human measurement, you will design coding manuals, scale automated quality assurance pipelines, and lead statistical calibration. Your work will ensure our global RLHF data maximizes construct validity, minimizes measurement error, and delivers the clinical-grade accuracy required to train safe, capable AI. In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1652044/000165204426000018/goog-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1652044/000165204426000018/goog-20251231.htm" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "https://blog.google/company-news/outreach-and-initiatives/diversity/international-womens-day-2022/" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "https://blog.google/company-news/outreach-and-initiatives/diversity/international-womens-day-2022/" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckfUsgM2J03gIs1E0rU85v1aFzlGinMPns22ahJTmBDZwEjsACxwdTHAP8xSzbfwNnb76W2gJSaS_592VLbzKOBpXCMzMptjZne9s3UQl4kpnhb14LkEQx028Q7B9Ww%3D%3D_V2&loc=US&title=Technical+Program+Manager+III", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "07713e7b89dc17083cf3027797eb05be", "title": "Senior Data Scientist", "employer_name": "Johnson & Johnson", "employer_slug": "johnson-johnson", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-09T00:00:00.000Z", "apply_url": "https://jj.wd5.myworkdayjobs.com/JJ/job/Bangalore-Karnataka-India/Senior-Data-Scientist_R-080985", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Data Scientist 2 Locations posted: Posted 3 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/200406/000020040626000016/jnj-20251228.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/200406/000020040626000016/jnj-20251228.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Technology", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.3a616abf1310ba527f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "07713e7b89dc17083cf3027797eb05be", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:07713e7b89dc17083cf3027797eb05be:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/200406/000020040626000016/jnj-20251228.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/200406/000020040626000016/jnj-20251228.htm" ], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jj.wd5.myworkdayjobs.com/JJ/job/Bangalore-Karnataka-India/Senior-Data-Scientist_R-080985", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jj.wd5.myworkdayjobs.com/JJ/job/Bangalore-Karnataka-India/Senior-Data-Scientist_R-080985", "source_values": [ "rule" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - Wikidata", "source_label": "Wikidata", "href": null, "source_values": [ "wikidata_sparql_industry" ], "checked_at": null } } }, { "id": "295ce13786db4dde0d9d5a7b45f9a585", "title": "Alternant.e en développement logiciel embarqué C++ - H/F", "employer_name": "GE Vernova", "employer_slug": "ge-vernova", "location_text": "Villeurbanne", "country": "FR", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-05-12T00:00:00.000Z", "apply_url": "https://gevernova.wd5.myworkdayjobs.com/Vernova_ExternalSite/job/Villeurbanne/Alternante-en-dveloppement-logiciel-embarqu-C-----H-F_R5040338-2", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Alternant.e en développement logiciel embarqué C++ - H/F Villeurbanne posted: Posted 30+ Days Ago", "parental_leave_weeks": 10, "non_birth_parent_leave_weeks": 10, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.gevernova.com/sustainability/documents/Sustainability/ge2022_sustainability_report_print_ada.pdf", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 85, "benefit_verified": true, "benefit_last_verified": "2026-05-07", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1996810/000199681026000015/gev-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1996810/000199681026000015/gev-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.gevernova.com/sustainability/documents/Sustainability/ge2022_sustainability_report_print_ada.pdf", "db_column": "parental_leave_weeks", "source_url": "https://www.gevernova.com/sustainability/documents/Sustainability/ge2022_sustainability_report_print_ada.pdf" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://www.gevernova.com/sustainability/documents/Sustainability/ge2022_sustainability_report_print_ada.pdf", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://www.gevernova.com/sustainability/documents/Sustainability/ge2022_sustainability_report_print_ada.pdf" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.6cd37b9daba2ec893c", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "295ce13786db4dde0d9d5a7b45f9a585", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "other", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:295ce13786db4dde0d9d5a7b45f9a585:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": "Employer benefits source", "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1996810/000199681026000015/gev-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1996810/000199681026000015/gev-20251231.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://gevernova.wd5.myworkdayjobs.com/Vernova_ExternalSite/job/Villeurbanne/Alternante-en-dveloppement-logiciel-embarqu-C-----H-F_R5040338-2", "source_values": [ "https://www.gevernova.com/sustainability/documents/Sustainability/ge2022_sustainability_report_print_ada.pdf" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://gevernova.wd5.myworkdayjobs.com/Vernova_ExternalSite/job/Villeurbanne/Alternante-en-dveloppement-logiciel-embarqu-C-----H-F_R5040338-2", "source_values": [ "https://www.gevernova.com/sustainability/documents/Sustainability/ge2022_sustainability_report_print_ada.pdf" ], "checked_at": "2026-05-07" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-05-07" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://gevernova.wd5.myworkdayjobs.com/Vernova_ExternalSite/job/Villeurbanne/Alternante-en-dveloppement-logiciel-embarqu-C-----H-F_R5040338-2", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "employer_industry": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": null, "source_values": [ "source_sector" ], "checked_at": null } } }, { "id": "aeb214adbfdb839d82a5bf4322db7926", "title": "Network Engineer", "employer_name": "Jump Trading", "employer_slug": "jump-trading", "location_text": "Chicago or New York", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 150000, "salary_max": 200000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 150000, "base_salary_max": 200000, "salary_disclosed": true, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2025-05-28T22:39:51.000Z", "apply_url": "https://www.jumptrading.com/hr/job?gh_jid=6928404", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Network Engineer Chicago or New York Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Being part of our Network Infrastructure Team, you'll get a chance to work on one of the highest performance networks in class with some of the brightest minds in the industry. We are looking for exceptional talent to help us take our infrastructure to the next level. What You'll Do: - Design, configure and support our global network infrastructure, collaborating across multiple teams to optimize for proprietary low latency trading applications. - Support and troubleshoot a hybrid wireless and wired high performance network environment - Take", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "bonus_offered": { "field": "bonus_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "bonus_offered" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.4561c5d7c9339e9c27", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "aeb214adbfdb839d82a5bf4322db7926", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description matched \"latent\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Network Engineer Chicago or New York Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Being part of our Network Infrastructure Team, you'll get a chance to work on one of the highest performance networks in class with some of the brightest minds in the industry. We are looking for exceptional talent to help us take our infrastructure to the next level. What You'll Do: - Design, configure and support our global network infrastructure, collaborating across multiple teams to optimize for proprietary low latency trading applications. - Support and troubleshoot a hybrid wireless and wired high performance network environment - Take", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:aeb214adbfdb839d82a5bf4322db7926:description:latent", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 90, "mobile_priority": 1, "ui": { "icon": "Search", "tone": "match", "href": null } }, { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.f2f18747f58d7a053b", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "aeb214adbfdb839d82a5bf4322db7926", "signal_type": "query_match", "display_text": "Description: \"latent\"", "tooltip": "Description contains \"latent\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Network Engineer Chicago or New York Jump Trading Group is committed to world class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting collaboration and mutual respect. At Jump, research outcomes drive more than superior risk adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Trading Infrastructure is a global organization of engineers who architect, build and maintain our world-class infrastructure. From colo design/implementation, to optimizing our exchange connectivity, to building world class low latent Wide Area Networks, we leverage research and automation to consistently adapt and innovate our infrastructure to scale and drive our trading and evolving business. Being part of our Network Infrastructure Team, you'll get a chance to work on one of the highest performance networks in class with some of the brightest minds in the industry. We are looking for exceptional talent to help us take our infrastructure to the next level. What You'll Do: - Design, configure and support our global network infrastructure, collaborating across multiple teams to optimize for proprietary low latency trading applications. - Support and troubleshoot a hybrid wireless and wired high performance network environment - Take", "matched_input": "latent", "derivation_source": "row_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "row_field_contains", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": true, "priority": 80, "mobile_priority": 2, "ui": { "icon": "SearchCheck", "tone": "match", "href": null } } ], "parental_leave_source_label": null, "k401_match_source_label": null, "k401_contribution_source_label": null, "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=6928404", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=6928404", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=6928404", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=6928404", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.jumptrading.com/hr/job?gh_jid=6928404", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "1930577bae0960762330c8b38db02b3b", "title": "Senior Scientist I, Biology", "employer_name": "Novartis", "employer_slug": "novartis", "location_text": "Cambridge (USA)", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": false, "equity_included_source": null, "equity_type": [ "none" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-11T00:00:00.000Z", "apply_url": "https://novartis.wd3.myworkdayjobs.com/Internal_Careers_for_Acquired_Entities/job/Cambridge-USA/Scientist---Biology_REQ-10079959-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Scientist I, Biology Cambridge (USA) posted: Posted Yesterday", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": null, "employer_industry_source": null, "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.2561a9cde2f7b56d0f", "query_id": "gq_e62e1d3d210eb8539503", "job_id": "1930577bae0960762330c8b38db02b3b", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "data", "matched_input": "latent", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_e62e1d3d210eb8539503:1930577bae0960762330c8b38db02b3b:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "equity_included": { "tier": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://novartis.wd3.myworkdayjobs.com/Internal_Careers_for_Acquired_Entities/job/Cambridge-USA/Scientist---Biology_REQ-10079959-1", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://novartis.wd3.myworkdayjobs.com/Internal_Careers_for_Acquired_Entities/job/Cambridge-USA/Scientist---Biology_REQ-10079959-1", "source_values": [ "rule" ], "checked_at": null } } } ], "total": 72, "page": 1, "per_page": 100, "applied_filters": { "q": "Latent" }, "hidden_unknown_benefits_count": 0, "hidden_quality_floor_count": 0, "quality_floor": "default", "search_profile": { "profile_id": "inv382.d6.pg_textsearch_bge_small_rrf.v1", "bm25_extension": "pg_textsearch", "vector_model": "BAAI/bge-small-en-v1.5", "vector_model_version": "v1.5", "fusion_method": "rrf" }, "explanation_context": { "non_trivial_query": false, "query_terms": [ "latent" ], "active_filter_keys": [], "visible_signal_limit": 3, "visible_trust_limit": 2 }, "event_context": { "query_context": { "query_id": "2fe0be95-c1c2-4271-8c95-1fcbcd914a8a", "issued_at": "2026-08-15T15:52:44.118Z", "route": "/jobs", "page": 1, "per_page": 100, "total_results": 72, "sort": "relevance", "ranking_policy": "hybrid_rrf_rerank", "ranking_policy_version": "inv382.jobs.search.v2", "query_hash": "hmac_sha256:lpUatswSWgFUbaMh4bbZpI7L408OO3l-k-he4bnT9n0", "filter_hash": "hmac_sha256:6CBErDPXOFVCSNkSGbTmuw6GxoMjwSLro0KqZMtxL_0", "query_features": { "has_q": true, "q_term_count": 1, "q_length_bucket": "1_15", "q_pii_redacted": false, "state_count": 0, "benefit_filters": [], "quality_floor": "default" }, "exposures": [ { "result_id": "250f198a797e19282e39beff11388017", "position": 1, "page_position": 1, "retrieval_features": { "bm25_score": -13.078261375427246, "vector_score": 0, "rrf_score": 0.014285714285714285, "rerank_score": 7.000533580780029, "text_match_title": true, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 65, "quality_score": 55 } }, { "result_id": "11d812ddf27180ceee47454c8d9540a2", "position": 2, "page_position": 2, "retrieval_features": { "bm25_score": -9.18787956237793, "vector_score": 0, "rrf_score": 0.012195121951219513, "rerank_score": 3.6593453884124756, "text_match_title": false, "text_match_employer": true, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "ec729f587c26d111c9009d28553671b5", "position": 3, "page_position": 3, "retrieval_features": { "bm25_score": -13.739463806152344, "vector_score": 0.28945491972834225, "rrf_score": 0.03128054740957967, "rerank_score": 2.494168519973755, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "6b75f5ecf6f2de87b8e7dcde9d6d307c", "position": 4, "page_position": 4, "retrieval_features": { "bm25_score": -13.078261375427246, "vector_score": 0.2986183166503906, "rrf_score": 0.02946912242686891, "rerank_score": 1.7637618780136108, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 68, "quality_score": 45 } }, { "result_id": "b318d36a537d5c981a20bc0d4e4961ff", "position": 5, "page_position": 5, "retrieval_features": { "bm25_score": -12.516363143920898, "vector_score": 0.31264046951291025, "rrf_score": 0.027417840375586856, "rerank_score": 1.3692692518234253, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "2d69c71e742fc5016d8e3b089821580f", "position": 6, "page_position": 6, "retrieval_features": { "bm25_score": -10.22496223449707, "vector_score": 0.2907792752516689, "rrf_score": 0.028693528693528692, "rerank_score": 1.2625749111175537, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "852cb099fc6e90855bdb0d64683e40c7", "position": 7, "page_position": 7, "retrieval_features": { "bm25_score": -13.078261375427246, "vector_score": 0.31976984025419586, "rrf_score": 0.027222222222222224, "rerank_score": 1.0550986528396606, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "184e67955a10761f4ca75d6e81a7b355", "position": 8, "page_position": 8, "retrieval_features": { "bm25_score": -13.400712013244629, "vector_score": 0.31997358798980713, "rrf_score": 0.02786377708978328, "rerank_score": 0.8721081018447876, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "b35b3a0826455d5fc4384a5185ffafee", "position": 9, "page_position": 9, "retrieval_features": { "bm25_score": -14.679153442382812, "vector_score": 0.3040025649069199, "rrf_score": 0.031054405392392875, "rerank_score": 0.7960451245307922, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "e7f05dab2a15320f176015582df46590", "position": 10, "page_position": 10, "retrieval_features": { "bm25_score": -14.41952896118164, "vector_score": 0.31013938067681535, "rrf_score": 0.03057889822595705, "rerank_score": 0.5660476088523865, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "216745e6ece50ac3de8b181fa077c6c9", "position": 11, "page_position": 11, "retrieval_features": { "bm25_score": -14.288264274597168, "vector_score": 0.2960248589515686, "rrf_score": 0.03125, "rerank_score": 0.5619439482688904, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "f0eaa196fd6ec73c73069e51f1ec86bc", "position": 12, "page_position": 12, "retrieval_features": { "bm25_score": -13.400712013244629, "vector_score": 0.315895676612854, "rrf_score": 0.028006267136701922, "rerank_score": -0.3181963562965393, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "98dfe1cb7b05893ec1e30995199c5f62", "position": 13, "page_position": 13, "retrieval_features": { "bm25_score": -14.288264274597168, "vector_score": 0.3111206293106079, "rrf_score": 0.02967032967032967, "rerank_score": -0.4455643594264984, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "6b7d235b61e0ae3db18b697885cff15d", "position": 14, "page_position": 14, "retrieval_features": { "bm25_score": -14.679153442382812, "vector_score": 0.2596310752520282, "rrf_score": 0.03278688524590164, "rerank_score": -1.347020149230957, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "f20921f8d4f1a0d7510deb252ea92b5c", "position": 15, "page_position": 15, "retrieval_features": { "bm25_score": -13.71153736114502, "vector_score": 0.3021603462504878, "rrf_score": 0.03007688828584351, "rerank_score": -1.6408865451812744, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "8c114cab70a119fbd9a6c167037772d6", "position": 16, "page_position": 16, "retrieval_features": { "bm25_score": -13.078261375427246, "vector_score": 0.3107426166534424, "rrf_score": 0.02819138376017471, "rerank_score": -1.718116283416748, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "4edefbb7131bd8c5059531b0abff3def", "position": 17, "page_position": 17, "retrieval_features": { "bm25_score": -11.460680961608887, "vector_score": 0, "rrf_score": 0.012987012987012988, "rerank_score": -1.9572352170944214, "text_match_title": false, "text_match_employer": true, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "d7efe96ba120586e2fa34d6c600063f6", "position": 18, "page_position": 18, "retrieval_features": { "bm25_score": -12.770964622497559, "vector_score": 0, "rrf_score": 0.013513513513513514, "rerank_score": -2.040708065032959, "text_match_title": false, "text_match_employer": true, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "526017073006c4c04a5e252908d79c5b", "position": 19, "page_position": 19, "retrieval_features": { "bm25_score": -11.792215347290039, "vector_score": 0.31326047699090853, "rrf_score": 0.027046783625730993, "rerank_score": -2.175602674484253, "text_match_title": false, "text_match_employer": true, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "61bec2d8271fc2082d64091bd642a8c8", "position": 20, "page_position": 20, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3229416608810425, "rrf_score": 0.012987012987012988, "rerank_score": -2.9680886268615723, "text_match_title": true, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 64, "quality_score": 35 } }, { "result_id": "680f5820241d3384e0944ce6c1d8b6a0", "position": 21, "page_position": 21, "retrieval_features": { "bm25_score": -9.85419750213623, "vector_score": 0, "rrf_score": 0.012345679012345678, "rerank_score": -11.01899528503418, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 95, "quality_score": 55 } }, { "result_id": "1b21fc8e414a5052cd0e937bfccc1271", "position": 22, "page_position": 22, "retrieval_features": { "bm25_score": -9.85419750213623, "vector_score": 0, "rrf_score": 0.0125, "rerank_score": -11.179211616516113, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 131, "quality_score": 55 } }, { "result_id": "b075d13d549a619b2c2af6f19c030570", "position": 23, "page_position": 23, "retrieval_features": { "bm25_score": 0, "vector_score": 0.32697729250720986, "rrf_score": 0.012658227848101266, "rerank_score": -11.189266204833984, "text_match_title": true, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 65, "quality_score": 35 } }, { "result_id": "f993529360e74a9e7931227fdbbc970d", "position": 24, "page_position": 24, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3334965109825134, "rrf_score": 0.012345679012345678, "rerank_score": -11.37212085723877, "text_match_title": true, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "a6d9cba0714096d91583d3b88c74cbad", "position": 25, "page_position": 25, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3137173652648926, "rrf_score": 0.0136986301369863, "rerank_score": -11.421422958374023, "text_match_title": true, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 35 } }, { "result_id": "f6af69517f70c9bb82cf1c05ae8ee6d8", "position": 26, "page_position": 26, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3339177370071411, "rrf_score": 0.012195121951219513, "rerank_score": -11.500072479248047, "text_match_title": true, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "8662b5b706f6b771690d637c90e9b117", "position": 27, "page_position": 27, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3257810875307854, "rrf_score": 0.01282051282051282, "rerank_score": -11.526171684265137, "text_match_title": true, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 35 } }, { "result_id": "0c124c60fb2a4f31f6f2fa5a6e5d1a4b", "position": 28, "page_position": 28, "retrieval_features": { "bm25_score": -7.31024169921875, "vector_score": 0, "rrf_score": 0.012048192771084338, "rerank_score": -11.530956268310547, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 64, "quality_score": 45 } }, { "result_id": "7dea8af3335a04c862c611ca3352697b", "position": 29, "page_position": 29, "retrieval_features": { "bm25_score": -10.22496223449707, "vector_score": 0, "rrf_score": 0.012658227848101266, "rerank_score": -11.551046371459961, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 143, "quality_score": 55 } }, { "result_id": "5a27c6e84dd83c1e44af91c31108b4b5", "position": 30, "page_position": 30, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3285936713218689, "rrf_score": 0.0125, "rerank_score": -11.571195602416992, "text_match_title": true, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "c8ac7d3436f8e019b671b3038acf9773", "position": 31, "page_position": 31, "retrieval_features": { "bm25_score": 0, "vector_score": 0.33456236124038696, "rrf_score": 0.012048192771084338, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 65, "quality_score": 35 } }, { "result_id": "abdc71a33112ca65abba12981e7cf015", "position": 32, "page_position": 32, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3346923589706421, "rrf_score": 0.011904761904761904, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "3554c65a748c82e62d5f5015bfdc9dfa", "position": 33, "page_position": 33, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.011904761904761904, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 254, "quality_score": 45 } }, { "result_id": "7a5a4a1c7fcf7545036388f7148357ce", "position": 34, "page_position": 34, "retrieval_features": { "bm25_score": 0, "vector_score": 0.33500043541190294, "rrf_score": 0.011764705882352941, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "4419ac05b680ab2318d03fa688ea0efe", "position": 35, "page_position": 35, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.011764705882352941, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 257, "quality_score": 55 } }, { "result_id": "78fc7e3d7bf24f490215b6158d58a8a8", "position": 36, "page_position": 36, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.011627906976744186, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "aca1fc7ee5c75a11c3e9269f94f2d716", "position": 37, "page_position": 37, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3352476754636946, "rrf_score": 0.011627906976744186, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 1213, "quality_score": 45 } }, { "result_id": "8b916600c629d7f11a4810120a39eb3f", "position": 38, "page_position": 38, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3358171582221985, "rrf_score": 0.011494252873563218, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "8c8f5409730a9324b3de0b7331c4688a", "position": 39, "page_position": 39, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.011494252873563218, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 79, "quality_score": 55 } }, { "result_id": "28058ce695bc780e60e12d820b158dae", "position": 40, "page_position": 40, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3358402452373639, "rrf_score": 0.011363636363636364, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "a0e18c9a20d7cd121b31b552216e17d0", "position": 41, "page_position": 41, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.011363636363636364, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 89, "quality_score": 55 } }, { "result_id": "6974da6a1c0d9baef672cf47cc769646", "position": 42, "page_position": 42, "retrieval_features": { "bm25_score": 0, "vector_score": 0.336219847202301, "rrf_score": 0.011235955056179775, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 64, "quality_score": 35 } }, { "result_id": "a12afcfbfcb678897f0161ea9c998094", "position": 43, "page_position": 43, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.011235955056179775, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 143, "quality_score": 55 } }, { "result_id": "a46e3a50a385571b3898bf6cd9c51b49", "position": 44, "page_position": 44, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.011111111111111112, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 73, "quality_score": 55 } }, { "result_id": "f56f38b1aabfd5be9880c83f022eccce", "position": 45, "page_position": 45, "retrieval_features": { "bm25_score": 0, "vector_score": 0.33631034709872765, "rrf_score": 0.011111111111111112, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "e78f8c8c4fb703b23d636bb0f22edee9", "position": 46, "page_position": 46, "retrieval_features": { "bm25_score": 0, "vector_score": 0.33675660710076016, "rrf_score": 0.01098901098901099, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "a82468c22792beb1eb357bc704831449", "position": 47, "page_position": 47, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.01098901098901099, "text_match_title": false, "text_match_employer": true, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "c8313331e34874272e8196c151b5ae52", "position": 48, "page_position": 48, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3378404974937439, "rrf_score": 0.010869565217391304, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 67, "quality_score": 45 } }, { "result_id": "aa256fcdbbf611dbb2fcc867a9166e06", "position": 49, "page_position": 49, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.010869565217391304, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 117, "quality_score": 55 } }, { "result_id": "46a45542f22b45cae8ba32e1311cb51b", "position": 50, "page_position": 50, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3379084266478749, "rrf_score": 0.010752688172043012, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "aa32db15d2a867591a0c387ae0afd75e", "position": 51, "page_position": 51, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.010752688172043012, "text_match_title": false, "text_match_employer": true, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "bb0d04218a8da3317164841ffa0351b7", "position": 52, "page_position": 52, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.010638297872340425, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 67, "quality_score": 55 } }, { "result_id": "0fb3a7aef366eadf293aa7972cb5ab22", "position": 53, "page_position": 53, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3381495475769043, "rrf_score": 0.010638297872340425, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "68f51b4fc29b8383df5d2290a6214413", "position": 54, "page_position": 54, "retrieval_features": { "bm25_score": 0, "vector_score": 0.33933115005493164, "rrf_score": 0.010526315789473684, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "becd4ae7bf95b685b69d8384b632a88e", "position": 55, "page_position": 55, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.010526315789473684, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 135, "quality_score": 55 } }, { "result_id": "350359ccc3ea832f4eb2f933488af777", "position": 56, "page_position": 56, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3402577435816667, "rrf_score": 0.010416666666666666, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 72, "quality_score": 35 } }, { "result_id": "f0e8e27418057e64db77a18b4acffdf9", "position": 57, "page_position": 57, "retrieval_features": { "bm25_score": -6.9370341300964355, "vector_score": 0, "rrf_score": 0.010416666666666666, "text_match_title": false, "text_match_employer": true, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "03007afa9856ce6e77a6c0d07bb00491", "position": 58, "page_position": 58, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.010309278350515464, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 163, "quality_score": 55 } }, { "result_id": "e61b7e2f726eaa137dc263b70ae948f4", "position": 59, "page_position": 59, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34069889783859253, "rrf_score": 0.010309278350515464, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "6001d1b12dea3cc458d4a840e6ef3c76", "position": 60, "page_position": 60, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3407565748240593, "rrf_score": 0.01020408163265306, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "0a4cb1dcb727faa0bf7cac71173e9429", "position": 61, "page_position": 61, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.01020408163265306, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 121, "quality_score": 55 } }, { "result_id": "9478389ec219ad4374a646e3dc664cdb", "position": 62, "page_position": 62, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34117750280037384, "rrf_score": 0.010101010101010102, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "19ccce00c89a81357b215c20ea393deb", "position": 63, "page_position": 63, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.010101010101010102, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 697, "quality_score": 55 } }, { "result_id": "22fd0b1381fa2b87b3865e594618396e", "position": 64, "page_position": 64, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.01, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 71, "quality_score": 55 } }, { "result_id": "db585fa4cf2e616dc32e9e76e23affd3", "position": 65, "page_position": 65, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3413252830505371, "rrf_score": 0.01, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 65, "quality_score": 35 } }, { "result_id": "48d067554d27c144a68402805a047854", "position": 66, "page_position": 66, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3414860963821411, "rrf_score": 0.009900990099009901, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 65, "quality_score": 35 } }, { "result_id": "27a9ff175bc105410e85ca8ff335a6ea", "position": 67, "page_position": 67, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009900990099009901, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 102, "quality_score": 55 } }, { "result_id": "8fd547752811919aceded7a9bbdb2c77", "position": 68, "page_position": 68, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34152621030807495, "rrf_score": 0.00980392156862745, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "283c21288bddb2cff9bbda4be0de3bec", "position": 69, "page_position": 69, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.00980392156862745, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 92, "quality_score": 45 } }, { "result_id": "2ce24d6b2e69f533019c701ab2343651", "position": 70, "page_position": 70, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3416391611099243, "rrf_score": 0.009708737864077669, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 67, "quality_score": 35 } }, { "result_id": "2e81315c43535580fb42f953b912d436", "position": 71, "page_position": 71, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009708737864077669, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 331, "quality_score": 55 } }, { "result_id": "2c3ae06a7218aec5abe0e3d66f19fac8", "position": 72, "page_position": 72, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3417206406593323, "rrf_score": 0.009615384615384616, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 68, "quality_score": 35 } }, { "result_id": "37c71f0d3a7b974d7d15b8d94ac87862", "position": 73, "page_position": 73, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009615384615384616, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 322, "quality_score": 55 } }, { "result_id": "2e4d7eff67cd8b32462c3dfae915c6c1", "position": 74, "page_position": 74, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34190805804181323, "rrf_score": 0.009523809523809525, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 79, "quality_score": 35 } }, { "result_id": "399840670d3bf58b1b6a03f7c6477d46", "position": 75, "page_position": 75, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009523809523809525, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 95, "quality_score": 55 } }, { "result_id": "d7f4bf40fa78ee2ab1b4b3eb90cc578a", "position": 76, "page_position": 76, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34201149094036154, "rrf_score": 0.009433962264150943, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "3b6e2e7f299b843ea394ba6d96eb1f76", "position": 77, "page_position": 77, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009433962264150943, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 122, "quality_score": 55 } }, { "result_id": "4837def32896686de9aa19decea9a9bc", "position": 78, "page_position": 78, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34202126610152206, "rrf_score": 0.009345794392523364, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "4af13d997185308926d1eb6868189a0f", "position": 79, "page_position": 79, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009345794392523364, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 135, "quality_score": 55 } }, { "result_id": "d5df879f003ce7aa0d5ce710f13cd5c2", "position": 80, "page_position": 80, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34218263626098633, "rrf_score": 0.009259259259259259, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "4c25a1eb305d0e911ac9e1386eab9340", "position": 81, "page_position": 81, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009259259259259259, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 224, "quality_score": 45 } }, { "result_id": "f66eb2d6cab0ac3abd9a2d72fec0623d", "position": 82, "page_position": 82, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34221337186018275, "rrf_score": 0.009174311926605505, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "57368d7332ac8327e964d85e3b7f1462", "position": 83, "page_position": 83, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009174311926605505, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 140, "quality_score": 55 } }, { "result_id": "0f92348b82570ffa5ff1bbebfd8184ac", "position": 84, "page_position": 84, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34221339225769043, "rrf_score": 0.00909090909090909, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 75, "quality_score": 35 } }, { "result_id": "81e5b09d1878c7212201ff833a329704", "position": 85, "page_position": 85, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.00909090909090909, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 96, "quality_score": 55 } }, { "result_id": "0fd4b28fa6574088b13da0c33a7d5e23", "position": 86, "page_position": 86, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3427559733390808, "rrf_score": 0.009009009009009009, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "83f242eca4af0e4255d32e27567f3bb5", "position": 87, "page_position": 87, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.009009009009009009, "text_match_title": false, "text_match_employer": true, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 66, "quality_score": 45 } }, { "result_id": "86103a3e6b675cde796c64b074fc1470", "position": 88, "page_position": 88, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3428851366043091, "rrf_score": 0.008928571428571428, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 65, "quality_score": 35 } }, { "result_id": "89fa21abfb4c7f2cba9dbab27de9459b", "position": 89, "page_position": 89, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.008928571428571428, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 240, "quality_score": 55 } }, { "result_id": "8b89fc531be6d517bbe1cae5a8511934", "position": 90, "page_position": 90, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.008849557522123894, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 94, "quality_score": 55 } }, { "result_id": "cabae4b572016a88e4a31415049bf605", "position": 91, "page_position": 91, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3429044876751597, "rrf_score": 0.008849557522123894, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 73, "quality_score": 35 } }, { "result_id": "98cbf59b32d589045a9cddf89457b12f", "position": 92, "page_position": 92, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.008771929824561403, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 96, "quality_score": 55 } }, { "result_id": "1b36d3828b0ad2c87f458c3312cfaaf0", "position": 93, "page_position": 93, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3430987996786472, "rrf_score": 0.008771929824561403, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "9c300a83307c0a73906b3c5160035cc1", "position": 94, "page_position": 94, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.008695652173913044, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 92, "quality_score": 55 } }, { "result_id": "ed0ffc10f8e1b106fa24bc391c1c9042", "position": 95, "page_position": 95, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3431273494286913, "rrf_score": 0.008695652173913044, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 65, "quality_score": 35 } }, { "result_id": "a3df9e7235631d1d94936e62c47c4314", "position": 96, "page_position": 96, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.008620689655172414, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 70, "quality_score": 55 } }, { "result_id": "07713e7b89dc17083cf3027797eb05be", "position": 97, "page_position": 97, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3431629538536072, "rrf_score": 0.008620689655172414, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 67, "quality_score": 35 } }, { "result_id": "295ce13786db4dde0d9d5a7b45f9a585", "position": 98, "page_position": 98, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34317139725855483, "rrf_score": 0.008547008547008548, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "aeb214adbfdb839d82a5bf4322db7926", "position": 99, "page_position": 99, "retrieval_features": { "bm25_score": -6.6000823974609375, "vector_score": 0, "rrf_score": 0.008547008547008548, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 443, "quality_score": 55 } }, { "result_id": "1930577bae0960762330c8b38db02b3b", "position": 100, "page_position": 100, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34329575300216675, "rrf_score": 0.00847457627118644, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 65, "quality_score": 35 } } ] }, "context_signature": "gZJVl-ByG5k9SotufhS_txs17kIWAGQB8DFPquubrds", "event_token": "gZJVl-ByG5k9SotufhS_txs17kIWAGQB8DFPquubrds" } }