{ "results": [ { "id": "0df3f141b48c0508a90f252240776726", "title": "Sr. Principal Machine Learning Engineer", "employer_name": "Comcast Corporation", "employer_slug": "comcast", "location_text": "CA - Virtual - E", "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": 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://comcast.wd5.myworkdayjobs.com/Comcast_Careers/job/CA---Virtual---E/Sr-Principal-Machine-Learning-Engineer_R438718", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Sr. Principal Machine Learning Engineer CA - Virtual - E posted: Posted 4 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://corporate.comcast.com/stories/comcast-nbcuniversal-top-5-best-workplaces-parents-2022", "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/1166691/000162828026004994/cmcsa-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/1166691/000162828026004994/cmcsa-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://corporate.comcast.com/stories/comcast-nbcuniversal-top-5-best-workplaces-parents-2022", "db_column": "parental_leave_weeks", "source_url": "https://corporate.comcast.com/stories/comcast-nbcuniversal-top-5-best-workplaces-parents-2022" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://corporate.comcast.com/stories/comcast-nbcuniversal-top-5-best-workplaces-parents-2022", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://corporate.comcast.com/stories/comcast-nbcuniversal-top-5-best-workplaces-parents-2022" } }, "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": "Media", "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.56d4575894a3d37d5a", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "0df3f141b48c0508a90f252240776726", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:0df3f141b48c0508a90f252240776726: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/1166691/000162828026004994/cmcsa-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1166691/000162828026004994/cmcsa-20251231.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://comcast.wd5.myworkdayjobs.com/Comcast_Careers/job/CA---Virtual---E/Sr-Principal-Machine-Learning-Engineer_R438718", "source_values": [ "https://corporate.comcast.com/stories/comcast-nbcuniversal-top-5-best-workplaces-parents-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://comcast.wd5.myworkdayjobs.com/Comcast_Careers/job/CA---Virtual---E/Sr-Principal-Machine-Learning-Engineer_R438718", "source_values": [ "https://corporate.comcast.com/stories/comcast-nbcuniversal-top-5-best-workplaces-parents-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://comcast.wd5.myworkdayjobs.com/Comcast_Careers/job/CA---Virtual---E/Sr-Principal-Machine-Learning-Engineer_R438718", "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://comcast.wd5.myworkdayjobs.com/Comcast_Careers/job/CA---Virtual---E/Sr-Principal-Machine-Learning-Engineer_R438718", "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": "edaf79bf36f48c6416de2701c96d7532", "title": "Inference Engineer, Robotics", "employer_name": "OpenAI", "employer_slug": "openai", "location_text": "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": 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": [ "cnbc_disruptor50", "cbinsights_unicorn", "a16z", "sequoia", "foundersfund" ], "posted_at": "2025-04-21T22:52:02.375Z", "apply_url": "https://jobs.ashbyhq.com/openai/3c2d1178-777f-4613-a084-75a3d37cd1af", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Inference Engineer, Robotics San Francisco, California, United States About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples' lives. About the Role We're looking for a GPU Inference Engineer to contribute to improvements in model serving efficiency for our Robotics research. This is a high-impact role where you'll drive initiatives to optimize inference performance and scalability. You'll also be engaged in model design, to help assist our researchers in developing inference-friendly models. This role is critical to scaling the team's broader goals - it will directly enable leadership to focus on higher-leverage initiatives by building a stronger technical foundation. In this role you will: - Perform engineering efforts focused on improving model serving, inference performance, and system efficiency - Drive optimizations from a kernel and data movement perspective to improve system throughput and reliability - Partner closely with research and product teams to ensure our models perform effectively at scale - Design, build, and improve critical serving infrastructure to support Robotics growth and reliability needs You might thrive in this role if you: - Have deep expertise in model performance optimization, particularly at the inference layer - Have a strong background in kernel-level systems, data movement, and low-level performance tuning -", "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": 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" }, "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": 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": 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.3e4f7b27c965f15c9e", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "edaf79bf36f48c6416de2701c96d7532", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Inference Engineer, Robotics San Francisco, California, United States About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples' lives. About the Role We're looking for a GPU Inference Engineer to contribute to improvements in model serving efficiency for our Robotics research. This is a high-impact role where you'll drive initiatives to optimize inference performance and scalability. You'll also be engaged in model design, to help assist our researchers in developing inference-friendly models. This role is critical to scaling the team's broader goals - it will directly enable leadership to focus on higher-leverage initiatives by building a stronger technical foundation. In this role you will: - Perform engineering efforts focused on improving model serving, inference performance, and system efficiency - Drive optimizations from a kernel and data movement perspective to improve system throughput and reliability - Partner closely with research and product teams to ensure our models perform effectively at scale - Design, build, and improve critical serving infrastructure to support Robotics growth and reliability needs You might thrive in this role if you: - Have deep expertise in model performance optimization, particularly at the inference layer - Have a strong background in kernel-level systems, data movement, and low-level performance tuning -", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:edaf79bf36f48c6416de2701c96d7532:description:kernel", "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.64c11446ba34b26ef5", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "edaf79bf36f48c6416de2701c96d7532", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Inference Engineer, Robotics San Francisco, California, United States About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples' lives. About the Role We're looking for a GPU Inference Engineer to contribute to improvements in model serving efficiency for our Robotics research. This is a high-impact role where you'll drive initiatives to optimize inference performance and scalability. You'll also be engaged in model design, to help assist our researchers in developing inference-friendly models. This role is critical to scaling the team's broader goals - it will directly enable leadership to focus on higher-leverage initiatives by building a stronger technical foundation. In this role you will: - Perform engineering efforts focused on improving model serving, inference performance, and system efficiency - Drive optimizations from a kernel and data movement perspective to improve system throughput and reliability - Partner closely with research and product teams to ensure our models perform effectively at scale - Design, build, and improve critical serving infrastructure to support Robotics growth and reliability needs You might thrive in this role if you: - Have deep expertise in model performance optimization, particularly at the inference layer - Have a strong background in kernel-level systems, data movement, and low-level performance tuning -", "matched_input": "kernel", "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": { "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://jobs.ashbyhq.com/openai/3c2d1178-777f-4613-a084-75a3d37cd1af", "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/openai/3c2d1178-777f-4613-a084-75a3d37cd1af", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "818a2809787b1b59fe1199bb744d9eda", "title": "Open-Source Machine Learning Engineer - EMEA Remote", "employer_name": "Hugging Face", "employer_slug": "hugging-face", "location_text": "Location not specified", "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": [ "cbinsights_unicorn", "sequoia" ], "posted_at": "2026-05-29T00:00:00.000Z", "apply_url": "https://apply.workable.com/j/81B46579FE", "apply_url_verified": false, "ats": "workable", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Open-Source Machine Learning Engineer - EMEA Remote", "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": 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" }, "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" }, "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.8ba5be7066ce3ed5f6", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "818a2809787b1b59fe1199bb744d9eda", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:818a2809787b1b59fe1199bb744d9eda: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://apply.workable.com/j/81B46579FE", "source_values": [ "rule:remote_status" ], "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 }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://apply.workable.com/j/81B46579FE", "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://apply.workable.com/j/81B46579FE", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "9d97f4832a0adc0c0ca9a8b01bd39516", "title": "Senior Applied Machine Learning Engineer", "employer_name": "Adobe Inc.", "employer_slug": "adobe", "location_text": "San Jose", "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": "2026-05-16T00:00:00.000Z", "apply_url": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Senior-Applied-Machine-Learning-Engineer--Typographic-Intelligence_R166553", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Applied Machine Learning Engineer San Jose posted: Posted 27 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.9a3d0737b78c27be55", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9d97f4832a0adc0c0ca9a8b01bd39516", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:9d97f4832a0adc0c0ca9a8b01bd39516: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-Applied-Machine-Learning-Engineer--Typographic-Intelligence_R166553", "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-Applied-Machine-Learning-Engineer--Typographic-Intelligence_R166553", "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-Applied-Machine-Learning-Engineer--Typographic-Intelligence_R166553", "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-Applied-Machine-Learning-Engineer--Typographic-Intelligence_R166553", "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": "037b95b1b749df4402f2a8509bb0315a", "title": "Software Engineer, Android BSP SE II", "employer_name": "Toast, Inc.", "employer_slug": "toast", "location_text": "Taipei City, Taiwan", "country": "TW", "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": "2026-05-12T16:53:08.000Z", "apply_url": "https://careers.toasttab.com/jobs?gh_jid=7892675", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Engineer, Android BSP SE II Taipei City, Taiwan Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. Now, more than ever, the Toast team is committed to our customers. We're taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we'll deliver on their needs for today, while investing in experiences that will power their restaurant of the future. Every day, many thousands of servers, hosts, and chefs use Toast POS (Point of Sale) to serve millions of guests in their restaurants. To make our restaurants successful, we need to develop products that install painlessly, work together seamlessly, and quickly guide restaurant staff and Toast team members to resolve any problems that occur. The Android OS team sits at the intersection of hardware and software and builds a platform that is the foundation for the Toast POS devices enabling creation of features that delight our customers. The Android OS team partners with a broad set of teams including POS R&D, Hardware R&D, Hardware Operations, Configuration Centers and Manufacturing partners. Engineers on this team operate across the full Android stack - kernel, BSP, framework, system apps, CI/CD,", "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" }, "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" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1650164/000165016426000057/tost-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" }, "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/1650164/000165016426000057/tost-20251231.htm", "source_fields": [ "equity_offered", "equity_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" } }, "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": "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": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.f7614ea4b6e4037823", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "037b95b1b749df4402f2a8509bb0315a", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Engineer, Android BSP SE II Taipei City, Taiwan Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. Now, more than ever, the Toast team is committed to our customers. We're taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we'll deliver on their needs for today, while investing in experiences that will power their restaurant of the future. Every day, many thousands of servers, hosts, and chefs use Toast POS (Point of Sale) to serve millions of guests in their restaurants. To make our restaurants successful, we need to develop products that install painlessly, work together seamlessly, and quickly guide restaurant staff and Toast team members to resolve any problems that occur. The Android OS team sits at the intersection of hardware and software and builds a platform that is the foundation for the Toast POS devices enabling creation of features that delight our customers. The Android OS team partners with a broad set of teams including POS R&D, Hardware R&D, Hardware Operations, Configuration Centers and Manufacturing partners. Engineers on this team operate across the full Android stack - kernel, BSP, framework, system apps, CI/CD,", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:037b95b1b749df4402f2a8509bb0315a:description:kernel", "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.7502ccd35bb9df4b88", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "037b95b1b749df4402f2a8509bb0315a", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Engineer, Android BSP SE II Taipei City, Taiwan Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy. Now, more than ever, the Toast team is committed to our customers. We're taking steps to help restaurants navigate these unprecedented times with technology, resources, and community. Our focus is on building the restaurant platform that helps restaurants adapt, take control, and get back to what they do best: building the businesses they love. And because our technology is purpose-built for restaurants, by restaurant people, restaurants can trust that we'll deliver on their needs for today, while investing in experiences that will power their restaurant of the future. Every day, many thousands of servers, hosts, and chefs use Toast POS (Point of Sale) to serve millions of guests in their restaurants. To make our restaurants successful, we need to develop products that install painlessly, work together seamlessly, and quickly guide restaurant staff and Toast team members to resolve any problems that occur. The Android OS team sits at the intersection of hardware and software and builds a platform that is the foundation for the Toast POS devices enabling creation of features that delight our customers. The Android OS team partners with a broad set of teams including POS R&D, Hardware R&D, Hardware Operations, Configuration Centers and Manufacturing partners. Engineers on this team operate across the full Android stack - kernel, BSP, framework, system apps, CI/CD,", "matched_input": "kernel", "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": { "equity_included": { "tier": "government", "verified": true, "label": "Verified - SEC 10-K", "source_label": "SEC 10-K", "href": "https://www.sec.gov/Archives/edgar/data/1650164/000165016426000057/tost-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1650164/000165016426000057/tost-20251231.htm" ], "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://careers.toasttab.com/jobs?gh_jid=7892675", "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://careers.toasttab.com/jobs?gh_jid=7892675", "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://careers.toasttab.com/jobs?gh_jid=7892675", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "312c9ddb409e06484bfe32b269e812b2", "title": "Sr Machine Learning Engineer", "employer_name": "Adobe Inc.", "employer_slug": "adobe", "location_text": "San Jose", "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": "2026-05-12T00:00:00.000Z", "apply_url": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Sr-Machine-Learning-Engineer-4_R165400-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Sr Machine Learning Engineer San Jose 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.28aa15b47e5408dfef", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "312c9ddb409e06484bfe32b269e812b2", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:312c9ddb409e06484bfe32b269e812b2: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/Sr-Machine-Learning-Engineer-4_R165400-1", "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/Sr-Machine-Learning-Engineer-4_R165400-1", "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/Sr-Machine-Learning-Engineer-4_R165400-1", "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://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Sr-Machine-Learning-Engineer-4_R165400-1", "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": "0d1dd932dee865870dac4b06f81e64f6", "title": "RISC-V AI / HPC & Agentic Software Engineering Lead", "employer_name": "TensTorrent", "employer_slug": "tenstorrent", "location_text": "North America", "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": 100000, "salary_max": 500000, "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": 100000, "base_salary_max": 500000, "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": "2026-02-20T19:46:58.000Z", "apply_url": "https://job-boards.greenhouse.io/tenstorrent/jobs/5056085007", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "RISC-V AI / HPC & Agentic Software Engineering Lead North America Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Tenstorrent is building next-generation RISC-V CPUs optimized for AI, high-performance computing, and emerging autonomous AI systems. As the RISC-V AI / HPC & Agentic Software Engineering Lead, you will operate at the hardware-software boundary, enabling AI, HPC, and agentic AI software stacks on Tenstorrent's RISC-V processors. This role is responsible for integrating and optimizing Tenstorrent's LLK (Low-Level Kernel) infrastructure for CPU execution, while leading the bring-up of a RISC-V-native agentic AI software stack - including runtime orchestration, distributed execution frameworks, memory systems, and developer tools required for goal-driven AI systems. You will work closely with CPU architects, microarchitecture teams, compiler engineers, and AI kernel developers to ensure tight alignment between software capabilities and RISC-V architectural features. This role is remote, based out of North America. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level,", "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_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" }, "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": "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": 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.b169a30743feb7a2f9", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "0d1dd932dee865870dac4b06f81e64f6", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "RISC-V AI / HPC & Agentic Software Engineering Lead North America Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Tenstorrent is building next-generation RISC-V CPUs optimized for AI, high-performance computing, and emerging autonomous AI systems. As the RISC-V AI / HPC & Agentic Software Engineering Lead, you will operate at the hardware-software boundary, enabling AI, HPC, and agentic AI software stacks on Tenstorrent's RISC-V processors. This role is responsible for integrating and optimizing Tenstorrent's LLK (Low-Level Kernel) infrastructure for CPU execution, while leading the bring-up of a RISC-V-native agentic AI software stack - including runtime orchestration, distributed execution frameworks, memory systems, and developer tools required for goal-driven AI systems. You will work closely with CPU architects, microarchitecture teams, compiler engineers, and AI kernel developers to ensure tight alignment between software capabilities and RISC-V architectural features. This role is remote, based out of North America. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level,", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:0d1dd932dee865870dac4b06f81e64f6:description:kernel", "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.ed4089d1342e0dbb12", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "0d1dd932dee865870dac4b06f81e64f6", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "RISC-V AI / HPC & Agentic Software Engineering Lead North America Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Tenstorrent is building next-generation RISC-V CPUs optimized for AI, high-performance computing, and emerging autonomous AI systems. As the RISC-V AI / HPC & Agentic Software Engineering Lead, you will operate at the hardware-software boundary, enabling AI, HPC, and agentic AI software stacks on Tenstorrent's RISC-V processors. This role is responsible for integrating and optimizing Tenstorrent's LLK (Low-Level Kernel) infrastructure for CPU execution, while leading the bring-up of a RISC-V-native agentic AI software stack - including runtime orchestration, distributed execution frameworks, memory systems, and developer tools required for goal-driven AI systems. You will work closely with CPU architects, microarchitecture teams, compiler engineers, and AI kernel developers to ensure tight alignment between software capabilities and RISC-V architectural features. This role is remote, based out of North America. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level,", "matched_input": "kernel", "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/tenstorrent/jobs/5056085007", "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/tenstorrent/jobs/5056085007", "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/tenstorrent/jobs/5056085007", "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/tenstorrent/jobs/5056085007", "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/tenstorrent/jobs/5056085007", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "1bb94449908b33b22fb377d7cc1a7174", "title": "Software Development Engineer, AI/ML, AWS Neuron, Model Inference", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Cupertino, California, 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": 165200, "salary_max": 223600, "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": 165200, "base_salary_max": 223600, "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": "2025-11-19T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work", "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": 5, "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.c7a5a9ed1825744f72", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "1bb94449908b33b22fb377d7cc1a7174", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:1bb94449908b33b22fb377d7cc1a7174:description:kernel", "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.ba9312a397acf6172f", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "1bb94449908b33b22fb377d7cc1a7174", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work", "matched_input": "kernel", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3128924/software-development-engineer-ai-ml-aws-neuron-model-inference", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "6e8d32c6827369e193a17bc82cbab13b", "title": "Werkstudent für Scientific Computing & Machine Learning (d/m/w)", "employer_name": "Airbus", "employer_slug": "airbus", "location_text": "Bremen Area", "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": "2026-06-09T00:00:00.000Z", "apply_url": "https://ag.wd3.myworkdayjobs.com/Airbus/job/Bremen-Area/Werkstudent-fr-Scientific-Computing---Machine-Learning--d-m-w-_JR10414181", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Werkstudent für Scientific Computing & Machine Learning (d/m/w) Bremen Area 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": 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": 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.c74d75cc40cf34e6d2", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "6e8d32c6827369e193a17bc82cbab13b", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:6e8d32c6827369e193a17bc82cbab13b: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://ag.wd3.myworkdayjobs.com/Airbus/job/Bremen-Area/Werkstudent-fr-Scientific-Computing---Machine-Learning--d-m-w-_JR10414181", "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://ag.wd3.myworkdayjobs.com/Airbus/job/Bremen-Area/Werkstudent-fr-Scientific-Computing---Machine-Learning--d-m-w-_JR10414181", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "1e7a83eb019171474cca65917cdce7cd", "title": "Senior Software Development Engineer, AI/ML, AWS Neuron, Model Inference", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Cupertino, California, 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": 193300, "salary_max": 261500, "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": 193300, "base_salary_max": 261500, "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": "2025-10-01T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to", "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": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "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": "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.d73a346c710642192e", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "1e7a83eb019171474cca65917cdce7cd", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Senior Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:1e7a83eb019171474cca65917cdce7cd:description:kernel", "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.6899ea83249800bb14", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "1e7a83eb019171474cca65917cdce7cd", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Senior Software Development Engineer, AI/ML, AWS Neuron, Model Inference Cupertino, California, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Inferentia and Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Inference Enablement and Acceleration team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to", "matched_input": "kernel", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "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/3098687/senior-software-development-engineer-ai-ml-aws-neuron-model-inference", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "23a18c9dea54575183aa870c301d25d2", "title": "Software Engineer, PhD, Early Careers, Platform System Software", "employer_name": "Google", "employer_slug": "google", "location_text": "Taipei, Taiwan", "country": "TW", "employment_type": "full_time", "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" ], "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": "2026-05-13T13:01:34.000Z", "apply_url": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckRa1aKqqcZ7B-idcPt5bVgWfAnTrPRmpLyExz4NHUzgIEjsACxwdTIeORRxjDVN2gvrXPJDfLell-tWR2E4qUFp5TMyNO2FPIbBMeatADJnW4OLyem9_E_UpLw2MOQ%3D%3D_V2&loc=TW&title=Software+Engineer", "apply_url_verified": false, "ats": "google_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Engineer, PhD, Early Careers, Platform System Software Taipei, Taiwan The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We're the driving team behind Google's groundbreaking innovations, empowering the development of our AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more. Develop C/C++ firmware and system software, including device drivers (UART, I2C, SPI), simulators, and emulators. Optimize low-level hardware interaction and peripheral control for new features. Focus on the secure aspects of the boot process, such as confidential compute, attestation, security, etc. Navigate up and down the stack as needed. Implement new BIOS features, sometimes debugging a kernel driver, or interacting with the silicon designers. Identify root causes of complex issues within the AI hypercomputer systems. Minimum qualifications: PhD degree in Computer Science, a related field, or equivalent practical experience. Experience coding in C/C++ or Python. Experience with embedded systems, system software, and software integration. Preferred qualifications: 1 year of experience with embedded Linux or Linux device drivers. Experience in code and system health", "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": 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": "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" }, "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" ] }, "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" }, "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" }, "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" ] }, "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" }, "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": 1, "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": "Technology", "employer_industry_source": "source_sector", "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": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.23190a052f2efad192", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "23a18c9dea54575183aa870c301d25d2", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Engineer, PhD, Early Careers, Platform System Software Taipei, Taiwan The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We're the driving team behind Google's groundbreaking innovations, empowering the development of our AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more. Develop C/C++ firmware and system software, including device drivers (UART, I2C, SPI), simulators, and emulators. Optimize low-level hardware interaction and peripheral control for new features. Focus on the secure aspects of the boot process, such as confidential compute, attestation, security, etc. Navigate up and down the stack as needed. Implement new BIOS features, sometimes debugging a kernel driver, or interacting with the silicon designers. Identify root causes of complex issues within the AI hypercomputer systems. Minimum qualifications: PhD degree in Computer Science, a related field, or equivalent practical experience. Experience coding in C/C++ or Python. Experience with embedded systems, system software, and software integration. Preferred qualifications: 1 year of experience with embedded Linux or Linux device drivers. Experience in code and system health", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:23a18c9dea54575183aa870c301d25d2:description:kernel", "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.b7ebd84c4ddc099332", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "23a18c9dea54575183aa870c301d25d2", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Engineer, PhD, Early Careers, Platform System Software Taipei, Taiwan The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We're the driving team behind Google's groundbreaking innovations, empowering the development of our AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more. Develop C/C++ firmware and system software, including device drivers (UART, I2C, SPI), simulators, and emulators. Optimize low-level hardware interaction and peripheral control for new features. Focus on the secure aspects of the boot process, such as confidential compute, attestation, security, etc. Navigate up and down the stack as needed. Implement new BIOS features, sometimes debugging a kernel driver, or interacting with the silicon designers. Identify root causes of complex issues within the AI hypercomputer systems. Minimum qualifications: PhD degree in Computer Science, a related field, or equivalent practical experience. Experience coding in C/C++ or Python. Experience with embedded systems, system software, and software integration. Preferred qualifications: 1 year of experience with embedded Linux or Linux device drivers. Experience in code and system health", "matched_input": "kernel", "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/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=CiUAL2FckRa1aKqqcZ7B-idcPt5bVgWfAnTrPRmpLyExz4NHUzgIEjsACxwdTIeORRxjDVN2gvrXPJDfLell-tWR2E4qUFp5TMyNO2FPIbBMeatADJnW4OLyem9_E_UpLw2MOQ%3D%3D_V2&loc=TW&title=Software+Engineer", "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=CiUAL2FckRa1aKqqcZ7B-idcPt5bVgWfAnTrPRmpLyExz4NHUzgIEjsACxwdTIeORRxjDVN2gvrXPJDfLell-tWR2E4qUFp5TMyNO2FPIbBMeatADJnW4OLyem9_E_UpLw2MOQ%3D%3D_V2&loc=TW&title=Software+Engineer", "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=CiUAL2FckRa1aKqqcZ7B-idcPt5bVgWfAnTrPRmpLyExz4NHUzgIEjsACxwdTIeORRxjDVN2gvrXPJDfLell-tWR2E4qUFp5TMyNO2FPIbBMeatADJnW4OLyem9_E_UpLw2MOQ%3D%3D_V2&loc=TW&title=Software+Engineer", "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=CiUAL2FckRa1aKqqcZ7B-idcPt5bVgWfAnTrPRmpLyExz4NHUzgIEjsACxwdTIeORRxjDVN2gvrXPJDfLell-tWR2E4qUFp5TMyNO2FPIbBMeatADJnW4OLyem9_E_UpLw2MOQ%3D%3D_V2&loc=TW&title=Software+Engineer", "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=CiUAL2FckRa1aKqqcZ7B-idcPt5bVgWfAnTrPRmpLyExz4NHUzgIEjsACxwdTIeORRxjDVN2gvrXPJDfLell-tWR2E4qUFp5TMyNO2FPIbBMeatADJnW4OLyem9_E_UpLw2MOQ%3D%3D_V2&loc=TW&title=Software+Engineer", "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": "e1cf088bd86646e9eb24623119fa3d9c", "title": "Platform Engineer Database", "employer_name": "Deutsche Post", "employer_slug": "deutsche-post", "location_text": "Utrecht, Utrecht, 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": 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": "2026-05-08T08:17:42.820Z", "apply_url": "https://dpdhlgroup.avature.net/nl_NL/jobs/ApplicationMethods?jobId=330293&source=careers.dhl.com", "apply_url_verified": false, "ats": "phenom_people", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Platform Engineer Database Utrecht, Utrecht, Netherlands Heb jij een passie voor het bedenken en opzetten van goede persistence en search oplossingen ten behoeve van product teams? Hebben databases als Oracle en Postgres geen geheimen voor je? Ben je ser...", "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:title_override", "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": "engineering", "role_function_source": "rule:title_override", "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.semantic.role_function.23d299a285b16e4ffb", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "e1cf088bd86646e9eb24623119fa3d9c", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:e1cf088bd86646e9eb24623119fa3d9c: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://dpdhlgroup.avature.net/nl_NL/jobs/ApplicationMethods?jobId=330293&source=careers.dhl.com", "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://dpdhlgroup.avature.net/nl_NL/jobs/ApplicationMethods?jobId=330293&source=careers.dhl.com", "source_values": [ "rule:title_override" ], "checked_at": null } } }, { "id": "2f21aa16f9dea523d45ba85944f15aa8", "title": "Staff Software Engineer, Accelerator Platform Software", "employer_name": "Google", "employer_slug": "google", "location_text": "Sunnyvale, 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": 207000, "salary_max": 301000, "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": 207000, "base_salary_max": 301000, "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": "2026-06-05T15:38:31.000Z", "apply_url": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "apply_url_verified": false, "ats": "google_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Staff Software Engineer, Accelerator Platform Software Sunnyvale, CA, USA Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. The Accelerator Platforms and Laboratory Team's mission is to empower Google's accelerator roadmap by engineering foundational hardware (Peripheral Component Interconnect Express, fabric, Input-Output Memory Management Unit) and low-level software (kernel, Firmware). We provide reliable, secure, and efficient accelerator-to-host connectivity to enable use cases. The team is also responsible for onboarding new types of domain-specific accelerators into Google data centers. As a Staff Software Engineer on the accelerator platforms team, you will have influence beyond, handling complex issues in low-level software and hardware interaction. The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough", "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" }, "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/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": "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": "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.e1b4daefbc83b41c35", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "2f21aa16f9dea523d45ba85944f15aa8", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Staff Software Engineer, Accelerator Platform Software Sunnyvale, CA, USA Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. The Accelerator Platforms and Laboratory Team's mission is to empower Google's accelerator roadmap by engineering foundational hardware (Peripheral Component Interconnect Express, fabric, Input-Output Memory Management Unit) and low-level software (kernel, Firmware). We provide reliable, secure, and efficient accelerator-to-host connectivity to enable use cases. The team is also responsible for onboarding new types of domain-specific accelerators into Google data centers. As a Staff Software Engineer on the accelerator platforms team, you will have influence beyond, handling complex issues in low-level software and hardware interaction. The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:2f21aa16f9dea523d45ba85944f15aa8:description:kernel", "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.ea1721e4844d4157a5", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "2f21aa16f9dea523d45ba85944f15aa8", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Staff Software Engineer, Accelerator Platform Software Sunnyvale, CA, USA Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. The Accelerator Platforms and Laboratory Team's mission is to empower Google's accelerator roadmap by engineering foundational hardware (Peripheral Component Interconnect Express, fabric, Input-Output Memory Management Unit) and low-level software (kernel, Firmware). We provide reliable, secure, and efficient accelerator-to-host connectivity to enable use cases. The team is also responsible for onboarding new types of domain-specific accelerators into Google data centers. As a Staff Software Engineer on the accelerator platforms team, you will have influence beyond, handling complex issues in low-level software and hardware interaction. The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough", "matched_input": "kernel", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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=CiUAL2FckSOnZm8NlY9IT5JEaiG-6wHjtKaBhM4PhYn4CLSiUA-qEjsACxwdTJAxIDbR8MMm0qT99F9CYJNVI1lEBamHQZOpwS4dWjWYGx_WqV2TigMsdMbPlTlLej__qYIUrg%3D%3D_V2&loc=US&title=Staff+Software+Engineer", "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": "2a30650f200d14d27865d2933464ac0b", "title": "Machine Learning Engineer - II", "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/Noida/Machine-Learning-Engineer---II_R167946", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Engineer - II 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": "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": "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.ae4e432dbeb228f1ad", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "2a30650f200d14d27865d2933464ac0b", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:2a30650f200d14d27865d2933464ac0b: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/Noida/Machine-Learning-Engineer---II_R167946", "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/Noida/Machine-Learning-Engineer---II_R167946", "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/Noida/Machine-Learning-Engineer---II_R167946", "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/Noida/Machine-Learning-Engineer---II_R167946", "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": "30804f7131cb27927bfa9fce7eeda365", "title": "AI Engineer", "employer_name": "Infosys Consulting", "employer_slug": "infosys-consulting", "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": "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://sjobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25633&siteid=5439&PageType=JobDetails&jobid=2246803", "apply_url_verified": false, "ats": "brassring_kenexa", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "AI Engineer Location - Bucharest,Timisoara,Cluj,Sibiu Business Unit - ADM Job Description Today, the corporate landscape is dynamic and the world ahead is full of possibilities! None of the amazing things we do at Infosys would be possible without an equally amazing culture, the environment where ideas can flourish and where you are empowered to move forward as far as your ideas will take you. At Infosys, we assure that your career will never stand still, we will inspire you to build what's next and we will navigate further together. Our journey of learnability, values and trusted relationships with our clients continue to be the cornerstones of our organization and these values are upheld only because of our people. Your role - Autonomous task agents that plan, reason, and use tools to complete multi-step workflows -campaign QA, comms research, briefing preparation, document workflows. - Multi-agent systems where specialized agents collaborate under an orchestrator (e.g. researcher / writer / reviewer patterns). - Long-running agents with persistent memory, human-in-the-loop checkpoints, and approval gates suitable for enterprise use. Required - Strong programming skills in Python (typed, tested, production-ready). TypeScript / Node.js a plus. - Hands-on production experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI, or open-weight models). - Demonstrated experience designing agentic systems - fluency with at least one agent framework (LangGraph, AutoGen, CrewAI, Semantic Kernel) AND practical familiarity with multiple agent design patterns (ReAct, plan-and-execute, reflection, multi-agent). - Practical experience with retrieval systems and at least one vector database (pgvector, Pinecone, Weaviate, Azure", "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" }, "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" }, "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": 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": "Professional Services", "employer_industry_source": "source_sector", "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.b7733c12d17476f172", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "30804f7131cb27927bfa9fce7eeda365", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "AI Engineer Location - Bucharest,Timisoara,Cluj,Sibiu Business Unit - ADM Job Description Today, the corporate landscape is dynamic and the world ahead is full of possibilities! None of the amazing things we do at Infosys would be possible without an equally amazing culture, the environment where ideas can flourish and where you are empowered to move forward as far as your ideas will take you. At Infosys, we assure that your career will never stand still, we will inspire you to build what's next and we will navigate further together. Our journey of learnability, values and trusted relationships with our clients continue to be the cornerstones of our organization and these values are upheld only because of our people. Your role - Autonomous task agents that plan, reason, and use tools to complete multi-step workflows -campaign QA, comms research, briefing preparation, document workflows. - Multi-agent systems where specialized agents collaborate under an orchestrator (e.g. researcher / writer / reviewer patterns). - Long-running agents with persistent memory, human-in-the-loop checkpoints, and approval gates suitable for enterprise use. Required - Strong programming skills in Python (typed, tested, production-ready). TypeScript / Node.js a plus. - Hands-on production experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI, or open-weight models). - Demonstrated experience designing agentic systems - fluency with at least one agent framework (LangGraph, AutoGen, CrewAI, Semantic Kernel) AND practical familiarity with multiple agent design patterns (ReAct, plan-and-execute, reflection, multi-agent). - Practical experience with retrieval systems and at least one vector database (pgvector, Pinecone, Weaviate, Azure", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:30804f7131cb27927bfa9fce7eeda365:description:kernel", "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.81888d4d1d2bf47508", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "30804f7131cb27927bfa9fce7eeda365", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "AI Engineer Location - Bucharest,Timisoara,Cluj,Sibiu Business Unit - ADM Job Description Today, the corporate landscape is dynamic and the world ahead is full of possibilities! None of the amazing things we do at Infosys would be possible without an equally amazing culture, the environment where ideas can flourish and where you are empowered to move forward as far as your ideas will take you. At Infosys, we assure that your career will never stand still, we will inspire you to build what's next and we will navigate further together. Our journey of learnability, values and trusted relationships with our clients continue to be the cornerstones of our organization and these values are upheld only because of our people. Your role - Autonomous task agents that plan, reason, and use tools to complete multi-step workflows -campaign QA, comms research, briefing preparation, document workflows. - Multi-agent systems where specialized agents collaborate under an orchestrator (e.g. researcher / writer / reviewer patterns). - Long-running agents with persistent memory, human-in-the-loop checkpoints, and approval gates suitable for enterprise use. Required - Strong programming skills in Python (typed, tested, production-ready). TypeScript / Node.js a plus. - Hands-on production experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI, or open-weight models). - Demonstrated experience designing agentic systems - fluency with at least one agent framework (LangGraph, AutoGen, CrewAI, Semantic Kernel) AND practical familiarity with multiple agent design patterns (ReAct, plan-and-execute, reflection, multi-agent). - Practical experience with retrieval systems and at least one vector database (pgvector, Pinecone, Weaviate, Azure", "matched_input": "kernel", "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": { "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://sjobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25633&siteid=5439&PageType=JobDetails&jobid=2246803", "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://sjobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?partnerid=25633&siteid=5439&PageType=JobDetails&jobid=2246803", "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": "fc8e05cc161b1d7939831ca56a0a09ef", "title": "Principal Engineer - FPGA", "employer_name": "Baker Hughes", "employer_slug": "baker-hughes", "location_text": "US-NY-SKANEATELES-721 VISIONS DRIVE", "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" ], "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://bakerhughes.wd5.myworkdayjobs.com/BakerHughes/job/US-NY-SKANEATELES-721-VISIONS-DRIVE/Electrical-Engineer---FPGA_R156053-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Principal Engineer - FPGA US-NY-SKANEATELES-721 VISIONS DRIVE posted: Posted 30+ Days Ago", "parental_leave_weeks": 12, "non_birth_parent_leave_weeks": 8, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.bakerhughesbenefits.com/faq-categories/paid-parental-leave", "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/1701605/000170160526000007/bkr-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/1701605/000170160526000007/bkr-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.bakerhughesbenefits.com/faq-categories/paid-parental-leave", "db_column": "parental_leave_weeks", "source_url": "https://www.bakerhughesbenefits.com/faq-categories/paid-parental-leave" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://www.bakerhughesbenefits.com/faq-categories/paid-parental-leave", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://www.bakerhughesbenefits.com/faq-categories/paid-parental-leave" } }, "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": "Energy", "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.abbeb23932407710d7", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "fc8e05cc161b1d7939831ca56a0a09ef", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:fc8e05cc161b1d7939831ca56a0a09ef: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/1701605/000170160526000007/bkr-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1701605/000170160526000007/bkr-20251231.htm" ], "checked_at": null }, "parental_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://bakerhughes.wd5.myworkdayjobs.com/BakerHughes/job/US-NY-SKANEATELES-721-VISIONS-DRIVE/Electrical-Engineer---FPGA_R156053-1", "source_values": [ "https://www.bakerhughesbenefits.com/faq-categories/paid-parental-leave" ], "checked_at": "2026-05-07" }, "non_birth_parent_leave_weeks": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://bakerhughes.wd5.myworkdayjobs.com/BakerHughes/job/US-NY-SKANEATELES-721-VISIONS-DRIVE/Electrical-Engineer---FPGA_R156053-1", "source_values": [ "https://www.bakerhughesbenefits.com/faq-categories/paid-parental-leave" ], "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://bakerhughes.wd5.myworkdayjobs.com/BakerHughes/job/US-NY-SKANEATELES-721-VISIONS-DRIVE/Electrical-Engineer---FPGA_R156053-1", "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://bakerhughes.wd5.myworkdayjobs.com/BakerHughes/job/US-NY-SKANEATELES-721-VISIONS-DRIVE/Electrical-Engineer---FPGA_R156053-1", "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": "cf0f7fe7f33a5cde66d4d57159a6f8c0", "title": "Sr. Principal Computer Systems Analyst - Linux", "employer_name": "Northrop Grumman Corporation", "employer_slug": "northrop-grumman", "location_text": "United States-Maryland-Baltimore", "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": [ "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": "2026-06-12T10:25:24.000Z", "apply_url": "https://ngc.eightfold.ai/careers/job/1340071715135", "apply_url_verified": false, "ats": "eightfold", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Sr. Principal Computer Systems Analyst - Linux United States-Maryland-Baltimore", "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/1133421/000113342126000003/noc-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/1133421/000113342126000003/noc-20251231.htm", "source_fields": [ "equity_offered", "equity_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.role_function.ea738f01f8c6e18862", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "cf0f7fe7f33a5cde66d4d57159a6f8c0", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:cf0f7fe7f33a5cde66d4d57159a6f8c0: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/1133421/000113342126000003/noc-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1133421/000113342126000003/noc-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://ngc.eightfold.ai/careers/job/1340071715135", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null } } }, { "id": "30f17ad95365327962988f22991645c1", "title": "Member of Technical Staff - Infrastructure", "employer_name": "Steel Dynamics", "employer_slug": "steel-dynamics", "location_text": "Toronto", "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": "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": 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": "2026-03-26T02:23:40.894Z", "apply_url": "https://jobs.ashbyhq.com/steel/612cf7fc-d001-47ac-ad15-90bf8f6dc70e", "apply_url_verified": true, "ats": "ashby", "url_last_checked_alive": "2026-08-17T02:59:13.328Z", "removed_at": null, "description_excerpt": "Member of Technical Staff - Infrastructure Toronto About Steel Steel is building open-source browser infrastructure for AI agents and apps . We make it easy for developers to ship AI products that interact with the web using our Sessions API . With over 6,000 GitHub stars, dozens of paying customers, and millions of sessions served monthly, we grew our platform 50x in 2025 purely through word-of-mouth and our open-source community. Backed by world-class investors, we're building a small, highly motivated, talent-dense team that's shaping the future of how humans interact with the internet. More on the problems we're solving: https://docs.steel.dev/overview/intro-to-steel Public referral bonus: Introduce us to a candidate who joins Steel, and we'll send you $10,000 The Role We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our platform. Sample projects include... Building VM snapshotting that restores full browser state in under a second Staring at kernel traces to performance engineer our Sessions API Orchestrating millions of browser sessions across regions with sub-second cold starts Working with VMs, proxies, caches, task queues, and orchestration systems Shipping the observability and CI/CD stack from scratch Requirements: 4+ years writing high-quality, production code Experience building and operating high-performance, large-scale distributed systems (battle scars welcome) Strong cloud infrastructure skills (AWS, GCP) Strong knowledge of OS foundations - Linux kernel, file systems, containers, etc Track record in performance optimization (we love a good \"shaved 5ms\" story) Able to work in-person from our", "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": "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" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1022671/000110465926021395/stld-20251231x10k.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_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1022671/000110465926021395/stld-20251231x10k.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "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": "staff_plus", "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": "early", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "Industrial", "employer_industry_source": "source_sector", "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": 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.2bbb825b066c684bb2", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "30f17ad95365327962988f22991645c1", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - Infrastructure Toronto About Steel Steel is building open-source browser infrastructure for AI agents and apps . We make it easy for developers to ship AI products that interact with the web using our Sessions API . With over 6,000 GitHub stars, dozens of paying customers, and millions of sessions served monthly, we grew our platform 50x in 2025 purely through word-of-mouth and our open-source community. Backed by world-class investors, we're building a small, highly motivated, talent-dense team that's shaping the future of how humans interact with the internet. More on the problems we're solving: https://docs.steel.dev/overview/intro-to-steel Public referral bonus: Introduce us to a candidate who joins Steel, and we'll send you $10,000 The Role We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our platform. Sample projects include... Building VM snapshotting that restores full browser state in under a second Staring at kernel traces to performance engineer our Sessions API Orchestrating millions of browser sessions across regions with sub-second cold starts Working with VMs, proxies, caches, task queues, and orchestration systems Shipping the observability and CI/CD stack from scratch Requirements: 4+ years writing high-quality, production code Experience building and operating high-performance, large-scale distributed systems (battle scars welcome) Strong cloud infrastructure skills (AWS, GCP) Strong knowledge of OS foundations - Linux kernel, file systems, containers, etc Track record in performance optimization (we love a good \"shaved 5ms\" story) Able to work in-person from our", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:30f17ad95365327962988f22991645c1:description:kernel", "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.d0ee72ae8994ab7605", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "30f17ad95365327962988f22991645c1", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Member of Technical Staff - Infrastructure Toronto About Steel Steel is building open-source browser infrastructure for AI agents and apps . We make it easy for developers to ship AI products that interact with the web using our Sessions API . With over 6,000 GitHub stars, dozens of paying customers, and millions of sessions served monthly, we grew our platform 50x in 2025 purely through word-of-mouth and our open-source community. Backed by world-class investors, we're building a small, highly motivated, talent-dense team that's shaping the future of how humans interact with the internet. More on the problems we're solving: https://docs.steel.dev/overview/intro-to-steel Public referral bonus: Introduce us to a candidate who joins Steel, and we'll send you $10,000 The Role We are looking for strong engineers with experience and interest in designing, building, and maintaining the novel, high-performance systems that make up our platform. Sample projects include... Building VM snapshotting that restores full browser state in under a second Staring at kernel traces to performance engineer our Sessions API Orchestrating millions of browser sessions across regions with sub-second cold starts Working with VMs, proxies, caches, task queues, and orchestration systems Shipping the observability and CI/CD stack from scratch Requirements: 4+ years writing high-quality, production code Experience building and operating high-performance, large-scale distributed systems (battle scars welcome) Strong cloud infrastructure skills (AWS, GCP) Strong knowledge of OS foundations - Linux kernel, file systems, containers, etc Track record in performance optimization (we love a good \"shaved 5ms\" story) Able to work in-person from our", "matched_input": "kernel", "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.trust.apply.bdffba95591ecd408a", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "30f17ad95365327962988f22991645c1", "signal_type": "trust_signal", "display_text": "Apply link reachable when checked", "tooltip": "The apply URL was reachable during the last recorded crawl-time liveness check; this is not a live guarantee.", "source_binding": "row_trust_evidence", "source_field": "apply_url_verified", "source_value": true, "matched_input": true, "derivation_source": "verified_source_field", "candidate_evidence_id": null, "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "trust_source_exists", "counts_for_non_trivial_query": false, "truth_verified": true, "exact": true, "priority": 10, "mobile_priority": 11, "ui": { "icon": "ExternalLink", "tone": "verified", "href": null }, "evidence_source": { "source_url": "https://jobs.ashbyhq.com/steel/612cf7fc-d001-47ac-ad15-90bf8f6dc70e", "source_label": "Apply link", "source_date": "2026-08-17T02:59:13.328Z" } } ], "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": "2026-08-17T02:59:13.328Z", "apply_url_verification_status": "reachable_when_last_checked", "apply_url_verification_label": "Reachable when checked at 2026-08-17T02:59:13.328Z", "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/1022671/000110465926021395/stld-20251231x10k.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1022671/000110465926021395/stld-20251231x10k.htm" ], "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": "2026-06-13T01:18:42.546Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/steel/612cf7fc-d001-47ac-ad15-90bf8f6dc70e", "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/steel/612cf7fc-d001-47ac-ad15-90bf8f6dc70e", "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/steel/612cf7fc-d001-47ac-ad15-90bf8f6dc70e", "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": "375ec4534244886dc02c8836fd0058a6", "title": "Software Engineer II- AI/ML, AWS Neuron", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Seattle, Washington, 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": 143700, "salary_max": 194400, "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": 143700, "base_salary_max": 194400, "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": "2026-03-24T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10375046/software-engineer-ii-ai-ml-aws-neuron", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Engineer II- AI/ML, AWS Neuron Seattle, Washington, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Training Enablement and Foundation team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of", "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" }, "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.38f4645080ae158e4b", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "375ec4534244886dc02c8836fd0058a6", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Software Engineer II- AI/ML, AWS Neuron Seattle, Washington, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Training Enablement and Foundation team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:375ec4534244886dc02c8836fd0058a6:description:kernel", "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.95ad240d45f153c098", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "375ec4534244886dc02c8836fd0058a6", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Software Engineer II- AI/ML, AWS Neuron Seattle, Washington, USA The Annapurna Labs team at Amazon Web Services (AWS) builds AWS Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning accelerators, Inferentia and Trainium. The AWS Neuron SDK, developed by the Annapurna Labs team at AWS, is the backbone for accelerating deep learning and GenAI workloads on Amazon's Trainium ML accelerators. This comprehensive toolkit includes an ML compiler, runtime, and application framework that seamlessly integrates with popular ML frameworks like PyTorch and JAX enabling unparalleled ML inference and training performance. The Training Enablement and Foundation team is at the forefront of running a wide range of models and supporting novel architecture alongside maximizing their performance for AWS's custom ML accelerators. Working across the stack from PyTorch till the hardware-software boundary, our engineers build systematic infrastructure, innovate new methods and create high-performance kernels for ML functions, ensuring every compute unit is fine tuned for optimal performance for our customers' demanding workloads. We combine deep hardware knowledge with ML expertise to push the boundaries of what's possible in AI acceleration. As part of the broader Neuron organization, our team works across multiple technology layers - from frameworks and kernels and collaborate with compiler to runtime and collectives. We not only optimize current performance but also contribute to future architecture designs, working closely with customers to enable their models and ensure optimal performance. This role offers a unique opportunity to work at the intersection of", "matched_input": "kernel", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "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/10375046/software-engineer-ii-ai-ml-aws-neuron", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "5af8037ef6e59ac07b4e9195ba3134d8", "title": "Principal Engineer (Machine Learning)", "employer_name": "CyberArk", "employer_slug": "cyberark", "location_text": "Office - USA - CA - Headquarters", "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": "2026-06-01T00:00:00.000Z", "apply_url": "https://paloaltonetworks.wd5.myworkdayjobs.com/panwexternalcareers/job/Office---USA---CA---Headquarters/Principal-Software-Engineer--Machine-Learning-_JR-015882", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Principal Engineer (Machine Learning) Office - USA - CA - Headquarters posted: Posted 11 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://benefits.paloaltonetworks.com/api/1.0/download/396", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 77, "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" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://benefits.paloaltonetworks.com/api/1.0/download/396", "db_column": "parental_leave_weeks", "source_url": "https://benefits.paloaltonetworks.com/api/1.0/download/396" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "https://benefits.paloaltonetworks.com/api/1.0/download/396", "db_column": "non_birth_parent_leave_weeks", "source_url": "https://benefits.paloaltonetworks.com/api/1.0/download/396" } }, "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": "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.a4b34176280f97ce94", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "5af8037ef6e59ac07b4e9195ba3134d8", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:5af8037ef6e59ac07b4e9195ba3134d8: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": "inferred", "verified": false, "label": "Inferred - source not recorded", "source_label": "source not recorded", "href": null, "source_values": [], "checked_at": null }, "parental_leave_weeks": { "tier": "government", "verified": true, "label": "Verified - O*NET", "source_label": "O*NET", "href": "https://benefits.paloaltonetworks.com/api/1.0/download/396", "source_values": [ "https://benefits.paloaltonetworks.com/api/1.0/download/396" ], "checked_at": "2026-06-07T00:00:00Z" }, "non_birth_parent_leave_weeks": { "tier": "government", "verified": true, "label": "Verified - O*NET", "source_label": "O*NET", "href": "https://benefits.paloaltonetworks.com/api/1.0/download/396", "source_values": [ "https://benefits.paloaltonetworks.com/api/1.0/download/396" ], "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://paloaltonetworks.wd5.myworkdayjobs.com/panwexternalcareers/job/Office---USA---CA---Headquarters/Principal-Software-Engineer--Machine-Learning-_JR-015882", "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://paloaltonetworks.wd5.myworkdayjobs.com/panwexternalcareers/job/Office---USA---CA---Headquarters/Principal-Software-Engineer--Machine-Learning-_JR-015882", "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": "76bcfc5f41f8999908dab5ee3c34d5d7", "title": "Machine Learning Engineer, Firefly Services", "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/Seattle/Machine-Learning-Engineer--Firefly-Services_R168300", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Engineer, Firefly Services 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": "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": "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.6a3b483abd7669b28f", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "76bcfc5f41f8999908dab5ee3c34d5d7", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:76bcfc5f41f8999908dab5ee3c34d5d7: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/Seattle/Machine-Learning-Engineer--Firefly-Services_R168300", "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/Seattle/Machine-Learning-Engineer--Firefly-Services_R168300", "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/Seattle/Machine-Learning-Engineer--Firefly-Services_R168300", "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/Seattle/Machine-Learning-Engineer--Firefly-Services_R168300", "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": "3a8ec7e2e8601e7baaeb5a0d01229d2a", "title": "ML Research Engineer - Hardware Codesign", "employer_name": "OpenAI", "employer_slug": "openai", "location_text": "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": 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": true, "top_startup_sources": [ "cnbc_disruptor50", "cbinsights_unicorn", "a16z", "sequoia", "foundersfund" ], "posted_at": "2026-01-13T18:28:07.796Z", "apply_url": "https://jobs.ashbyhq.com/openai/5931abef-191b-417e-89f1-1d06f00e908c", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "ML Research Engineer - Hardware Codesign San Francisco, California, United States About the Team OpenAI's Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI's supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role We're seeking a Research-Hardware Codesign Engineer to operate at the boundary between model research and silicon/system architecture. You'll help shape the numerics, architecture, and technology bets of future OpenAI silicon in collaboration with both Research and Hardware. Your work will include debugging gaps between rooflines and reality, writing quantization kernels, derisking numerics via model evals, quantifying system architecture tradeoffs, and implementing novel numeric RTL. This is a hands-on role for people who go looking for hard problems, get to ground truth, and drive it to production. Strong prioritization and clear, honest communication are essential. Location: San Francisco, CA (Hybrid: 3 days/week onsite) Relocation assistance available. In this role you will: - Build on our roofline simulator to track evolving workloads, and deliver analyses that quantify the impact of system architecture decisions and support technology pathfinding. - Debug gaps between performance simulation and real measurements; clearly communicate root cause, bottlenecks, and invalid assumptions. - Write emulation kernels for low-precision numerics", "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": 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" }, "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" }, "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": "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": 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.3fd015855dade64673", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "3a8ec7e2e8601e7baaeb5a0d01229d2a", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "ML Research Engineer - Hardware Codesign San Francisco, California, United States About the Team OpenAI's Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI's supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role We're seeking a Research-Hardware Codesign Engineer to operate at the boundary between model research and silicon/system architecture. You'll help shape the numerics, architecture, and technology bets of future OpenAI silicon in collaboration with both Research and Hardware. Your work will include debugging gaps between rooflines and reality, writing quantization kernels, derisking numerics via model evals, quantifying system architecture tradeoffs, and implementing novel numeric RTL. This is a hands-on role for people who go looking for hard problems, get to ground truth, and drive it to production. Strong prioritization and clear, honest communication are essential. Location: San Francisco, CA (Hybrid: 3 days/week onsite) Relocation assistance available. In this role you will: - Build on our roofline simulator to track evolving workloads, and deliver analyses that quantify the impact of system architecture decisions and support technology pathfinding. - Debug gaps between performance simulation and real measurements; clearly communicate root cause, bottlenecks, and invalid assumptions. - Write emulation kernels for low-precision numerics", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:3a8ec7e2e8601e7baaeb5a0d01229d2a:description:kernel", "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.6f47a0d10ca3aaba38", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "3a8ec7e2e8601e7baaeb5a0d01229d2a", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "ML Research Engineer - Hardware Codesign San Francisco, California, United States About the Team OpenAI's Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI's supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role We're seeking a Research-Hardware Codesign Engineer to operate at the boundary between model research and silicon/system architecture. You'll help shape the numerics, architecture, and technology bets of future OpenAI silicon in collaboration with both Research and Hardware. Your work will include debugging gaps between rooflines and reality, writing quantization kernels, derisking numerics via model evals, quantifying system architecture tradeoffs, and implementing novel numeric RTL. This is a hands-on role for people who go looking for hard problems, get to ground truth, and drive it to production. Strong prioritization and clear, honest communication are essential. Location: San Francisco, CA (Hybrid: 3 days/week onsite) Relocation assistance available. In this role you will: - Build on our roofline simulator to track evolving workloads, and deliver analyses that quantify the impact of system architecture decisions and support technology pathfinding. - Debug gaps between performance simulation and real measurements; clearly communicate root cause, bottlenecks, and invalid assumptions. - Write emulation kernels for low-precision numerics", "matched_input": "kernel", "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": { "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://jobs.ashbyhq.com/openai/5931abef-191b-417e-89f1-1d06f00e908c", "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/openai/5931abef-191b-417e-89f1-1d06f00e908c", "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://jobs.ashbyhq.com/openai/5931abef-191b-417e-89f1-1d06f00e908c", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "43615ea2a6abe4eec913b6a60348def5", "title": "Sr Software Engineer, Graviton Software, Annapurna Labs", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Austin, Texas, 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": 168100, "salary_max": 227400, "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": 168100, "base_salary_max": 227400, "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": true, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-04-23T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10401493/sr-software-engineer-graviton-software-annapurna-labs", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Sr Software Engineer, Graviton Software, Annapurna Labs Austin, Texas, USA This opportunity is open for Austin, TX. The AWS Graviton Software team is seeking Software Engineers to optimize performance for AWS Graviton. Graviton delivers the best price/performance in AWS data centers. For the past 2 years, Graviton has powered the majority of new EC2 capacity and is used by over 90% of our largest customers. You'll drive performance optimization across open source projects, internal services, and customer applications, working on everything from Linux kernel development to C++/Rust debugging to assembly-level optimization. Our team optimizes a diverse range of applications including databases, web services, games, video encoding, ML, and HPC workloads. This doesn't mean you have or will have all those skills, but you'll have a chance to learn from those who do. This is a unique opportunity to impact how software runs in AWS, while growing your technical breadth and depth. Key job responsibilities As a Graviton Software Developer, you will: Performance Optimization & Analysis - Enhance architectures and algorithms for optimal performance - Use tools like Aperf, sysstat, sysctl, perf to identify bottlenecks - Monitor and analyze processor, OS, and workload metrics - Optimize software performance for AWS customers and AWS services (such as S3, Lambda, EMR, ElastiCache, RDS ..etc) Open Source Development - Contribute to Linux kernel improvements - Contribute to various other open source projects - Develop analysis frameworks and automation systems Tool(s) Development - Enhance APerf (our open-source Rust-based performance tool) - Create new performance analysis", "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" }, "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" }, "surrogacy_assistance_offered": { "field": "surrogacy_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "surrogacy_assistance_offered" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "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": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.b0420ac92a9fcc3abd", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "43615ea2a6abe4eec913b6a60348def5", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description matched \"kernel\" from your search.", "source_binding": "bm25_field_evidence", "source_field": "description_excerpt", "source_value": "Sr Software Engineer, Graviton Software, Annapurna Labs Austin, Texas, USA This opportunity is open for Austin, TX. The AWS Graviton Software team is seeking Software Engineers to optimize performance for AWS Graviton. Graviton delivers the best price/performance in AWS data centers. For the past 2 years, Graviton has powered the majority of new EC2 capacity and is used by over 90% of our largest customers. You'll drive performance optimization across open source projects, internal services, and customer applications, working on everything from Linux kernel development to C++/Rust debugging to assembly-level optimization. Our team optimizes a diverse range of applications including databases, web services, games, video encoding, ML, and HPC workloads. This doesn't mean you have or will have all those skills, but you'll have a chance to learn from those who do. This is a unique opportunity to impact how software runs in AWS, while growing your technical breadth and depth. Key job responsibilities As a Graviton Software Developer, you will: Performance Optimization & Analysis - Enhance architectures and algorithms for optimal performance - Use tools like Aperf, sysstat, sysctl, perf to identify bottlenecks - Monitor and analyze processor, OS, and workload metrics - Optimize software performance for AWS customers and AWS services (such as S3, Lambda, EMR, ElastiCache, RDS ..etc) Open Source Development - Contribute to Linux kernel improvements - Contribute to various other open source projects - Develop analysis frameworks and automation systems Tool(s) Development - Enhance APerf (our open-source Rust-based performance tool) - Create new performance analysis", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:43615ea2a6abe4eec913b6a60348def5:description:kernel", "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.495dbb415cd4408528", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "43615ea2a6abe4eec913b6a60348def5", "signal_type": "query_match", "display_text": "Description: \"kernel\"", "tooltip": "Description contains \"kernel\" from your search.", "source_binding": "token_match", "source_field": "description_excerpt", "source_value": "Sr Software Engineer, Graviton Software, Annapurna Labs Austin, Texas, USA This opportunity is open for Austin, TX. The AWS Graviton Software team is seeking Software Engineers to optimize performance for AWS Graviton. Graviton delivers the best price/performance in AWS data centers. For the past 2 years, Graviton has powered the majority of new EC2 capacity and is used by over 90% of our largest customers. You'll drive performance optimization across open source projects, internal services, and customer applications, working on everything from Linux kernel development to C++/Rust debugging to assembly-level optimization. Our team optimizes a diverse range of applications including databases, web services, games, video encoding, ML, and HPC workloads. This doesn't mean you have or will have all those skills, but you'll have a chance to learn from those who do. This is a unique opportunity to impact how software runs in AWS, while growing your technical breadth and depth. Key job responsibilities As a Graviton Software Developer, you will: Performance Optimization & Analysis - Enhance architectures and algorithms for optimal performance - Use tools like Aperf, sysstat, sysctl, perf to identify bottlenecks - Monitor and analyze processor, OS, and workload metrics - Optimize software performance for AWS customers and AWS services (such as S3, Lambda, EMR, ElastiCache, RDS ..etc) Open Source Development - Contribute to Linux kernel improvements - Contribute to various other open source projects - Develop analysis frameworks and automation systems Tool(s) Development - Enhance APerf (our open-source Rust-based performance tool) - Create new performance analysis", "matched_input": "kernel", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "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/10401493/sr-software-engineer-graviton-software-annapurna-labs", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" }, "surrogacy_assistance_offered": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://www.amazon.jobs/en/jobs/10401493/sr-software-engineer-graviton-software-annapurna-labs", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "7ca2c45245a5ffe0e10dd72529c36760", "title": "Graduate Talent (System and Hardware Enabling Engineer)", "employer_name": "Intel", "employer_slug": "intel", "location_text": "Malaysia, Penang", "country": "MY", "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": "2026-06-04T00:00:00.000Z", "apply_url": "https://intel.wd1.myworkdayjobs.com/External/job/Malaysia-Penang/Graduate-Talent--System-and-Hardware-Enabling-Engineer-_JR0284586", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Graduate Talent (System and Hardware Enabling Engineer) Malaysia, Penang 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": "2026-06-13T01:18:42.546Z", "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/50863/000005086326000011/intc-20251227.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/50863/000005086326000011/intc-20251227.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": "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.398ee3c3e9137314b3", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "7ca2c45245a5ffe0e10dd72529c36760", "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": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:7ca2c45245a5ffe0e10dd72529c36760: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/50863/000005086326000011/intc-20251227.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/50863/000005086326000011/intc-20251227.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://intel.wd1.myworkdayjobs.com/External/job/Malaysia-Penang/Graduate-Talent--System-and-Hardware-Enabling-Engineer-_JR0284586", "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://intel.wd1.myworkdayjobs.com/External/job/Malaysia-Penang/Graduate-Talent--System-and-Hardware-Enabling-Engineer-_JR0284586", "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 } } } ], "total": 494, "page": 12, "per_page": 24, "applied_filters": { "q": "KERNEL" }, "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": [ "kernel" ], "active_filter_keys": [], "visible_signal_limit": 3, "visible_trust_limit": 2 }, "event_context": { "query_context": { "query_id": "99d8724d-dc1a-4362-84d6-5aec517b0518", "issued_at": "2026-08-20T09:51:17.515Z", "route": "/jobs", "page": 12, "per_page": 24, "total_results": 494, "sort": "relevance", "ranking_policy": "hybrid_rrf_rerank", "ranking_policy_version": "inv382.jobs.search.v2", "query_hash": "hmac_sha256:0LuO8hbKwiSPDLWH2KrX4ZTyKdtZxwx0euwulc8sABU", "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": "0df3f141b48c0508a90f252240776726", "position": 265, "page_position": 1, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3449917826061367, "rrf_score": 0.0041841004184100415, "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": "edaf79bf36f48c6416de2701c96d7532", "position": 266, "page_position": 2, "retrieval_features": { "bm25_score": -7.4449591636657715, "vector_score": 0, "rrf_score": 0.0041841004184100415, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 485, "quality_score": 45 } }, { "result_id": "818a2809787b1b59fe1199bb744d9eda", "position": 267, "page_position": 3, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34503992259026495, "rrf_score": 0.004166666666666667, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 83, "quality_score": 35 } }, { "result_id": "9d97f4832a0adc0c0ca9a8b01bd39516", "position": 268, "page_position": 4, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3450594934785657, "rrf_score": 0.004149377593360996, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 96, "quality_score": 35 } }, { "result_id": "037b95b1b749df4402f2a8509bb0315a", "position": 269, "page_position": 5, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004149377593360996, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 99, "quality_score": 45 } }, { "result_id": "312c9ddb409e06484bfe32b269e812b2", "position": 270, "page_position": 6, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3450705799418331, "rrf_score": 0.004132231404958678, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 100, "quality_score": 35 } }, { "result_id": "0d1dd932dee865870dac4b06f81e64f6", "position": 271, "page_position": 7, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004132231404958678, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 180, "quality_score": 45 } }, { "result_id": "1bb94449908b33b22fb377d7cc1a7174", "position": 272, "page_position": 8, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.00411522633744856, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 274, "quality_score": 55 } }, { "result_id": "6e8d32c6827369e193a17bc82cbab13b", "position": 273, "page_position": 9, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34518064913980373, "rrf_score": 0.00411522633744856, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 72, "quality_score": 35 } }, { "result_id": "1e7a83eb019171474cca65917cdce7cd", "position": 274, "page_position": 10, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004098360655737705, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 323, "quality_score": 55 } }, { "result_id": "23a18c9dea54575183aa870c301d25d2", "position": 275, "page_position": 11, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004081632653061225, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 98, "quality_score": 45 } }, { "result_id": "e1cf088bd86646e9eb24623119fa3d9c", "position": 276, "page_position": 12, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3453174417832563, "rrf_score": 0.004081632653061225, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 104, "quality_score": 45 } }, { "result_id": "2f21aa16f9dea523d45ba85944f15aa8", "position": 277, "page_position": 13, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.0040650406504065045, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 75, "quality_score": 55 } }, { "result_id": "2a30650f200d14d27865d2933464ac0b", "position": 278, "page_position": 14, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3453182177150754, "rrf_score": 0.0040650406504065045, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 100, "quality_score": 35 } }, { "result_id": "30804f7131cb27927bfa9fce7eeda365", "position": 279, "page_position": 15, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004048582995951417, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 68, "quality_score": 45 } }, { "result_id": "fc8e05cc161b1d7939831ca56a0a09ef", "position": 280, "page_position": 16, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3453311118286174, "rrf_score": 0.004048582995951417, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 100, "quality_score": 35 } }, { "result_id": "cf0f7fe7f33a5cde66d4d57159a6f8c0", "position": 281, "page_position": 17, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3453455361517933, "rrf_score": 0.004032258064516129, "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": "30f17ad95365327962988f22991645c1", "position": 282, "page_position": 18, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004032258064516129, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 147, "quality_score": 45 } }, { "result_id": "375ec4534244886dc02c8836fd0058a6", "position": 283, "page_position": 19, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004016064257028112, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 149, "quality_score": 55 } }, { "result_id": "5af8037ef6e59ac07b4e9195ba3134d8", "position": 284, "page_position": 20, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34569940890673234, "rrf_score": 0.004016064257028112, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 80, "quality_score": 35 } }, { "result_id": "76bcfc5f41f8999908dab5ee3c34d5d7", "position": 285, "page_position": 21, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34574721182899293, "rrf_score": 0.004, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 100, "quality_score": 35 } }, { "result_id": "3a8ec7e2e8601e7baaeb5a0d01229d2a", "position": 286, "page_position": 22, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.004, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 218, "quality_score": 45 } }, { "result_id": "43615ea2a6abe4eec913b6a60348def5", "position": 287, "page_position": 23, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.00398406374501992, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 119, "quality_score": 55 } }, { "result_id": "7ca2c45245a5ffe0e10dd72529c36760", "position": 288, "page_position": 24, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3457611777017383, "rrf_score": 0.00398406374501992, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 77, "quality_score": 35 } } ] }, "context_signature": "jYUZmPhR5Kphg0xXoaI_T34-VV2O3i-bDBjYJCAdriA", "event_token": "jYUZmPhR5Kphg0xXoaI_T34-VV2O3i-bDBjYJCAdriA" } }