{ "results": [ { "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 } } }, { "id": "160451bb4574c3b5bed32d9db241da1e", "title": "On-Device ML Integration Engineer, Graphics, Games and Machine Learning", "employer_name": "Apple", "employer_slug": "apple", "location_text": "Cupertino, United States of America", "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": 181100, "salary_max": 318400, "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": 181100, "base_salary_max": 318400, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": 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": "2026-05-21T16:25:51.930Z", "apply_url": "https://jobs.apple.com/en-us/details/200642883/on-device-ml-integration-engineer-graphics-games-and-machine-learning?team=MLAI", "apply_url_verified": false, "ats": "apple_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "On-Device ML Integration Engineer, Graphics, Games and Machine Learning Cupertino, United States of America Imagine being at the forefront of an evolution where cutting-edge AI meets the elegance of Apple silicon. The On-Device Machine Learning team transforms groundbreaking research into practical applications, enabling billions of Apple devices to run powerful AI models locally, privately, and efficiently. We stand at the unique intersection of research, software engineering, hardware engineering, and product development, making Apple the leading destination for machine learning innovation. Our team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding cutting-edge architectures to embedded systems, developing optimization toolkits for model compression and acceleration, building ML compilers and runtimes for efficient execution, and creating comprehensive benchmarking and debugging toolchains. This infrastructure forms the backbone of Apple's machine learning workflows across Camera, Siri, Health, Vision, and other core experiences, contributing to the overall Apple Intelligence ecosystem. If you are passionate about the technical challenges of running sophisticated ML models across all devices, from resource-constrained devices to powerful clusters, and eager to directly impact how machine learning operates across the Apple ecosystem, this role presents an exciting opportunity to work on the next generation of intelligent experiences on Apple platforms. We are seeking a seasoned ML Engineer with broad experience and an in depth understanding of machine learning, models and infrastructure. In this senior role, you will ensure Apple's inference stack allows integrating ML workflows end-to-end with excellent user experience, flawless functionality, and maximum performance.", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "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" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "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/320193/000032019325000079/aapl-20250927.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_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" } }, "seniority": "senior", "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": "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.semantic.role_function.b197e774d154e57ddb", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "160451bb4574c3b5bed32d9db241da1e", "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:160451bb4574c3b5bed32d9db241da1e:vector", "rerank_evidence_id": null, "taxonomy_alias_id": null, "oracle_verification_method": "candidate_evidence_exists", "counts_for_non_trivial_query": true, "truth_verified": true, "exact": false, "priority": 74, "mobile_priority": 2, "ui": { "icon": "Sparkles", "tone": "match", "href": null } } ], "source_label": "DOL Form 5500 filing", "parental_leave_source_label": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200642883/on-device-ml-integration-engineer-graphics-games-and-machine-learning?team=MLAI", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200642883/on-device-ml-integration-engineer-graphics-games-and-machine-learning?team=MLAI", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200642883/on-device-ml-integration-engineer-graphics-games-and-machine-learning?team=MLAI", "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/320193/000032019325000079/aapl-20250927.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.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.apple.com/en-us/details/200642883/on-device-ml-integration-engineer-graphics-games-and-machine-learning?team=MLAI", "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.apple.com/en-us/details/200642883/on-device-ml-integration-engineer-graphics-games-and-machine-learning?team=MLAI", "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.apple.com/en-us/details/200642883/on-device-ml-integration-engineer-graphics-games-and-machine-learning?team=MLAI", "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": "5533e2e54fd1a1cfde14904cd34cc3df", "title": "Machine Learning Performance Engineer, Annapurna Labs", "employer_name": "Amazon", "employer_slug": "amazon", "location_text": "Tel Aviv-Yafo, Tel Aviv, ISR", "country": "IL", "employment_type": "internship", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": true, "childcare_subsidy": true, "fertility_family_building_benefits": true, "adoption_assistance_offered": true, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2025-12-08T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/3138794/machine-learning-performance-engineer-annapurna-labs", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Performance Engineer, Annapurna Labs Tel Aviv-Yafo, Tel Aviv, ISR Our team is responsible for the AWS Neuron software stack, which powers Generative AI and other advanced ML workloads on AWS's custom-built ML accelerators - Inferentia and Trainium. These accelerators deliver best-in-class performance and cost-efficiency for ML inference and training in the cloud. We're building a new core group of engineers in TLV (Tel Aviv) to drive innovation in ML systems performance and software. As a Machine Learning Performance Engineer, you'll help shape the direction of the team from the ground up and work on: Optimizing system performance across the entire ML software stack Analyzing high-performance ML workloads running on Annapurna hardware Developing high-performance kernels for critical ML operations Enhancing the Neuron SDK to improve developer experience and system capabilities Collaborating across Compiler, Frameworks, and Hardware teams to maximize end-to-end performance As part of the Performance Engineering Team, you'll contribute to projects involving instruction scheduling, memory management, parallelism, kernel optimization, and compiler enhancements to maximize end-to-end performance. This is a unique opportunity to be at the intersection of ML and systems within AWS, helping to build the future of AI infrastructure - right here in Tel Aviv. Key job responsibilities Our engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You will: Solve challenging technical problems, often ones not solved before, at every layer of the stack. Design, implement, test, deploy and maintain innovative software solutions to transform service performance,", "parental_leave_weeks": 6, "non_birth_parent_leave_weeks": 6, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 52, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "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" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1018724/000101872426000004/amzn-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "childcare_subsidy": { "field": "childcare_subsidy", "source": "llm:greenfield:inv_371_postfix", "db_column": "childcare_subsidy" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "parental_leave_weeks": { "field": "parental_leave_weeks", "source": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families", "db_column": "parental_leave_weeks", "source_url": "https://www.aboutamazon.com/news/workplace/what-20-weeks-of-fully-paid-leave-does-for-Amazon-families" }, "mental_health_support": { "field": "mental_health_support", "source": "llm:greenfield:inv_371_postfix", "db_column": "mental_health_support" }, "adoption_assistance_offered": { "field": "adoption_assistance_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "adoption_assistance_offered" }, "non_birth_parent_leave_weeks": { "field": "non_birth_parent_leave_weeks", "source": "llm:greenfield:inv_371_postfix", "db_column": "non_birth_parent_leave_weeks" }, "fertility_family_building_benefits": { "field": "fertility_family_building_benefits", "source": "llm:greenfield:inv_371_postfix", "db_column": "fertility_family_building_benefits" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": null, "company_stage_source": null, "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 45, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.60208a2b087cdf7cbb", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "5533e2e54fd1a1cfde14904cd34cc3df", "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": "Machine Learning Performance Engineer, Annapurna Labs Tel Aviv-Yafo, Tel Aviv, ISR Our team is responsible for the AWS Neuron software stack, which powers Generative AI and other advanced ML workloads on AWS's custom-built ML accelerators - Inferentia and Trainium. These accelerators deliver best-in-class performance and cost-efficiency for ML inference and training in the cloud. We're building a new core group of engineers in TLV (Tel Aviv) to drive innovation in ML systems performance and software. As a Machine Learning Performance Engineer, you'll help shape the direction of the team from the ground up and work on: Optimizing system performance across the entire ML software stack Analyzing high-performance ML workloads running on Annapurna hardware Developing high-performance kernels for critical ML operations Enhancing the Neuron SDK to improve developer experience and system capabilities Collaborating across Compiler, Frameworks, and Hardware teams to maximize end-to-end performance As part of the Performance Engineering Team, you'll contribute to projects involving instruction scheduling, memory management, parallelism, kernel optimization, and compiler enhancements to maximize end-to-end performance. This is a unique opportunity to be at the intersection of ML and systems within AWS, helping to build the future of AI infrastructure - right here in Tel Aviv. Key job responsibilities Our engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You will: Solve challenging technical problems, often ones not solved before, at every layer of the stack. Design, implement, test, deploy and maintain innovative software solutions to transform service performance,", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:5533e2e54fd1a1cfde14904cd34cc3df: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.05ccaf06292e60bd4e", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "5533e2e54fd1a1cfde14904cd34cc3df", "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": "Machine Learning Performance Engineer, Annapurna Labs Tel Aviv-Yafo, Tel Aviv, ISR Our team is responsible for the AWS Neuron software stack, which powers Generative AI and other advanced ML workloads on AWS's custom-built ML accelerators - Inferentia and Trainium. These accelerators deliver best-in-class performance and cost-efficiency for ML inference and training in the cloud. We're building a new core group of engineers in TLV (Tel Aviv) to drive innovation in ML systems performance and software. As a Machine Learning Performance Engineer, you'll help shape the direction of the team from the ground up and work on: Optimizing system performance across the entire ML software stack Analyzing high-performance ML workloads running on Annapurna hardware Developing high-performance kernels for critical ML operations Enhancing the Neuron SDK to improve developer experience and system capabilities Collaborating across Compiler, Frameworks, and Hardware teams to maximize end-to-end performance As part of the Performance Engineering Team, you'll contribute to projects involving instruction scheduling, memory management, parallelism, kernel optimization, and compiler enhancements to maximize end-to-end performance. This is a unique opportunity to be at the intersection of ML and systems within AWS, helping to build the future of AI infrastructure - right here in Tel Aviv. Key job responsibilities Our engineers collaborate across diverse teams, projects, and environments to have a firsthand impact on our global customer base. You will: Solve challenging technical problems, often ones not solved before, at every layer of the stack. Design, implement, test, deploy and maintain innovative software solutions to transform service performance,", "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/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/3138794/machine-learning-performance-engineer-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/3138794/machine-learning-performance-engineer-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/3138794/machine-learning-performance-engineer-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/3138794/machine-learning-performance-engineer-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/3138794/machine-learning-performance-engineer-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/3138794/machine-learning-performance-engineer-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/3138794/machine-learning-performance-engineer-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/3138794/machine-learning-performance-engineer-annapurna-labs", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "5ecb6623f9deb68ef21766b89181d865", "title": "NPI Engineer, Embedded Firmware and Software, Emerging Accelerator 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": "2026-06-05T18:09:20.000Z", "apply_url": "https://www.google.com/about/careers/applications/signin?jobId=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+Engineer", "apply_url_verified": false, "ats": "google_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "NPI Engineer, Embedded Firmware and Software, Emerging Accelerator Software Sunnyvale, CA, USA Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. 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's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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. Accel Platforms and Lab Team's mission is to empower Google's accelerator roadmap by engineering foundational hardware (PCIe, fabric, IOMMU) and low-level software (kernel, firmware). We provide reliable, secure, and efficient accelerator-to-host connectivity to enable cutting-edge 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 Emerging Accelerator System team, you will have influence well beyond yourself, solving complex challenges in low-level software and hardware interaction. Your role is critical to Google's accelerator roadmap. The AI and Infrastructure team is redefining what's possible. We empower Google", "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" }, "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": null, "company_stage_source": null, "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.07dd3f649ee22bceb9", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "5ecb6623f9deb68ef21766b89181d865", "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": "NPI Engineer, Embedded Firmware and Software, Emerging Accelerator Software Sunnyvale, CA, USA Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. 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's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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. Accel Platforms and Lab Team's mission is to empower Google's accelerator roadmap by engineering foundational hardware (PCIe, fabric, IOMMU) and low-level software (kernel, firmware). We provide reliable, secure, and efficient accelerator-to-host connectivity to enable cutting-edge 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 Emerging Accelerator System team, you will have influence well beyond yourself, solving complex challenges in low-level software and hardware interaction. Your role is critical to Google's accelerator roadmap. The AI and Infrastructure team is redefining what's possible. We empower Google", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:5ecb6623f9deb68ef21766b89181d865: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.1db6653794f6a3cb99", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "5ecb6623f9deb68ef21766b89181d865", "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": "NPI Engineer, Embedded Firmware and Software, Emerging Accelerator Software Sunnyvale, CA, USA Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. 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's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. 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. Accel Platforms and Lab Team's mission is to empower Google's accelerator roadmap by engineering foundational hardware (PCIe, fabric, IOMMU) and low-level software (kernel, firmware). We provide reliable, secure, and efficient accelerator-to-host connectivity to enable cutting-edge 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 Emerging Accelerator System team, you will have influence well beyond yourself, solving complex challenges in low-level software and hardware interaction. Your role is critical to Google's accelerator roadmap. The AI and Infrastructure team is redefining what's possible. We empower Google", "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=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+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=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+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=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+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=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+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=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+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=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+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=CiUAL2FckYmPENDJ2HIAXZ_x9yAUhvBtb_pZFxZYhUEtR-sXuQieEjsACxwdTGjw6_EeqxgdAuIaUwrQ3FfReOv7etPyfqPBkdg3fYoXPAk9xWPDulTVTNhHUl55Q7JjXh-16w%3D%3D_V2&loc=US&title=NPI+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": "9a449e6bca7a0540b313195298334e14", "title": "Machine Learning Services Engineer - Firefly", "employer_name": "Adobe Inc.", "employer_slug": "adobe", "location_text": "Bucharest", "country": "RO", "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/Bucharest/Machine-Learning-Services-Engineer---Firefly_R164500", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Services Engineer - Firefly Bucharest 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.73c1d5f1c04271c656", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9a449e6bca7a0540b313195298334e14", "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:9a449e6bca7a0540b313195298334e14: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/Bucharest/Machine-Learning-Services-Engineer---Firefly_R164500", "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/Bucharest/Machine-Learning-Services-Engineer---Firefly_R164500", "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/Bucharest/Machine-Learning-Services-Engineer---Firefly_R164500", "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/Bucharest/Machine-Learning-Services-Engineer---Firefly_R164500", "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": "ade96efd59812c4f6635ec36243cadd7", "title": "Senior 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": "2026-05-18T00:00:00.000Z", "apply_url": "https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Senior-Machine-Learning-Engineer_R160087", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Engineer San Jose posted: Posted 25 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.1a28c29f524cc9f33d", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "ade96efd59812c4f6635ec36243cadd7", "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:ade96efd59812c4f6635ec36243cadd7: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-Machine-Learning-Engineer_R160087", "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-Machine-Learning-Engineer_R160087", "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-Machine-Learning-Engineer_R160087", "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-Machine-Learning-Engineer_R160087", "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": "615b3370b6e84af430fd64a7b7525b19", "title": "Embedded Firmware Engineer (Wi-Fi)", "employer_name": "Ubiquiti", "employer_slug": "ubiquiti", "location_text": "Hsinchu", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2025-12-22T09:24:02.000Z", "apply_url": "https://job-boards.greenhouse.io/ubiquiti/jobs/4087981009", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Embedded Firmware Engineer (Wi-Fi) Hsinchu About Ubiquiti At Ubiquiti Inc., we create technology platforms for Businesses, Smart Homes, and Internet Service Providers, driven by our goal to connect everyone, everywhere. To date, Ubiquiti has shipped over 100 million devices worldwide, from ISP networking products to next generation of IT solutions. Our growth is made possible by the dedicated team of hundreds behind the scenes. From software developers and product managers to designers and strategists, Team UI is driven to achieve our common goal: Rethinking IT. At Ubiquiti, you'll heighten your potential and broaden your horizons - all while shaping the future of connectivity. Responsibilities: - Develop and debug 802.11 WLAN/WiFi Driver/Firmware - Performance analysis, tuning on WLAN/WiFi Driver/Firmware - Analyze and troubleshoot WiFi issues through various debugging tools - Linux kernel/driver programming/debugging - Develop and implement test plans to verify and debug the implementation - Plan and drive technical discussions with internal teams and chip vendors Minimum Qualifications: - Strong coding skills in C/C++, shell scripting, Makefile - Experience with Wi-Fi standards, including 802.11ac/ax/be/k/v/r - Experience with Linux device driver development, Linux kernel patching, source management systems such as git - Experience in embedded system software programming - Proficiency in English - verbal and text - Proficiency in using debugging tools like gdb/strace/objdump, and kernel debug configs - A team player who is very self-motivated, loves difficult challenges, and is goal-driven - Strong communication skills, able to work both solo and in teams - At least 3yr+ experience as a", "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/1511737/000151173725000053/ubnt-20250630.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1511737/000151173725000053/ubnt-20250630.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": "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": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "doctorate", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.510923540516a3b772", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "615b3370b6e84af430fd64a7b7525b19", "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": "Embedded Firmware Engineer (Wi-Fi) Hsinchu About Ubiquiti At Ubiquiti Inc., we create technology platforms for Businesses, Smart Homes, and Internet Service Providers, driven by our goal to connect everyone, everywhere. To date, Ubiquiti has shipped over 100 million devices worldwide, from ISP networking products to next generation of IT solutions. Our growth is made possible by the dedicated team of hundreds behind the scenes. From software developers and product managers to designers and strategists, Team UI is driven to achieve our common goal: Rethinking IT. At Ubiquiti, you'll heighten your potential and broaden your horizons - all while shaping the future of connectivity. Responsibilities: - Develop and debug 802.11 WLAN/WiFi Driver/Firmware - Performance analysis, tuning on WLAN/WiFi Driver/Firmware - Analyze and troubleshoot WiFi issues through various debugging tools - Linux kernel/driver programming/debugging - Develop and implement test plans to verify and debug the implementation - Plan and drive technical discussions with internal teams and chip vendors Minimum Qualifications: - Strong coding skills in C/C++, shell scripting, Makefile - Experience with Wi-Fi standards, including 802.11ac/ax/be/k/v/r - Experience with Linux device driver development, Linux kernel patching, source management systems such as git - Experience in embedded system software programming - Proficiency in English - verbal and text - Proficiency in using debugging tools like gdb/strace/objdump, and kernel debug configs - A team player who is very self-motivated, loves difficult challenges, and is goal-driven - Strong communication skills, able to work both solo and in teams - At least 3yr+ experience as a", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:615b3370b6e84af430fd64a7b7525b19: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.19d5a7b9c575b23f26", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "615b3370b6e84af430fd64a7b7525b19", "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": "Embedded Firmware Engineer (Wi-Fi) Hsinchu About Ubiquiti At Ubiquiti Inc., we create technology platforms for Businesses, Smart Homes, and Internet Service Providers, driven by our goal to connect everyone, everywhere. To date, Ubiquiti has shipped over 100 million devices worldwide, from ISP networking products to next generation of IT solutions. Our growth is made possible by the dedicated team of hundreds behind the scenes. From software developers and product managers to designers and strategists, Team UI is driven to achieve our common goal: Rethinking IT. At Ubiquiti, you'll heighten your potential and broaden your horizons - all while shaping the future of connectivity. Responsibilities: - Develop and debug 802.11 WLAN/WiFi Driver/Firmware - Performance analysis, tuning on WLAN/WiFi Driver/Firmware - Analyze and troubleshoot WiFi issues through various debugging tools - Linux kernel/driver programming/debugging - Develop and implement test plans to verify and debug the implementation - Plan and drive technical discussions with internal teams and chip vendors Minimum Qualifications: - Strong coding skills in C/C++, shell scripting, Makefile - Experience with Wi-Fi standards, including 802.11ac/ax/be/k/v/r - Experience with Linux device driver development, Linux kernel patching, source management systems such as git - Experience in embedded system software programming - Proficiency in English - verbal and text - Proficiency in using debugging tools like gdb/strace/objdump, and kernel debug configs - A team player who is very self-motivated, loves difficult challenges, and is goal-driven - Strong communication skills, able to work both solo and in teams - At least 3yr+ experience as a", "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/1511737/000151173725000053/ubnt-20250630.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1511737/000151173725000053/ubnt-20250630.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://job-boards.greenhouse.io/ubiquiti/jobs/4087981009", "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/ubiquiti/jobs/4087981009", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/ubiquiti/jobs/4087981009", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "6cd82b05c0e9d8f1c9f6bfd8e8ed4b86", "title": "Software Engineering Manager, Triage Services and Infrastructure", "employer_name": "Apple", "employer_slug": "apple", "location_text": "Cupertino, United States of America", "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": 228100, "salary_max": 393800, "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": 228100, "base_salary_max": 393800, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": 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": "2026-02-26T23:05:42.613Z", "apply_url": "https://jobs.apple.com/en-us/details/200648961/software-engineering-manager-triage-services-and-infrastructure?team=SFTWR", "apply_url_verified": false, "ats": "apple_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Engineering Manager, Triage Services and Infrastructure Cupertino, United States of America The Core OS team is seeking an exceptional engineering manager to lead the team responsible for enabling Apple's operating systems to achieve world-class reliability. This team develops and owns mission-critical tools and services that detect, analyze, and classify kernel panics and low-level crashes across all Apple platforms. You will be partnering with engineering teams across Software, Hardware, and Silicon groups to drive and deliver the rock-solid OS reliability for over 2 billion currently active Apple devices and shape the future of system reliability across Apple's entire product ecosystem. Lead a team of engineers triaging kernel panics and critical system-level issues across all Apple platforms (macOS, iOS, watchOS, tvOS). Build intelligent automation pipelines that analyze, group, and prioritize failure signatures based on their reliability impact. Mentor engineers to design and develop advanced systems diagnostic and at-scale debug services to realize the vision of zero-iteration debugging and fully automated triage and root cause analysis. Develop telemetry-based dashboards to monitor at-scale panic/crash triage and analysis services to ensure they are working as expected and efficiently. Collaborate with Core OS, Hardware, Silicon, and other engineering teams to champion and advance improvements in debuggability, panic data quality, symbolication, and automation of triage and debug workflows. Build and manage a world-class Panic Triage & Tools team, developing senior systems engineers into technical leaders Define and execute the multi-year technical roadmap for platform triage and reliability, partnering with senior cross-functional leaders to align with Apple's", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "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" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" } }, "seniority": "mid", "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": "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": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.cc784e7311ab016921", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "6cd82b05c0e9d8f1c9f6bfd8e8ed4b86", "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 Engineering Manager, Triage Services and Infrastructure Cupertino, United States of America The Core OS team is seeking an exceptional engineering manager to lead the team responsible for enabling Apple's operating systems to achieve world-class reliability. This team develops and owns mission-critical tools and services that detect, analyze, and classify kernel panics and low-level crashes across all Apple platforms. You will be partnering with engineering teams across Software, Hardware, and Silicon groups to drive and deliver the rock-solid OS reliability for over 2 billion currently active Apple devices and shape the future of system reliability across Apple's entire product ecosystem. Lead a team of engineers triaging kernel panics and critical system-level issues across all Apple platforms (macOS, iOS, watchOS, tvOS). Build intelligent automation pipelines that analyze, group, and prioritize failure signatures based on their reliability impact. Mentor engineers to design and develop advanced systems diagnostic and at-scale debug services to realize the vision of zero-iteration debugging and fully automated triage and root cause analysis. Develop telemetry-based dashboards to monitor at-scale panic/crash triage and analysis services to ensure they are working as expected and efficiently. Collaborate with Core OS, Hardware, Silicon, and other engineering teams to champion and advance improvements in debuggability, panic data quality, symbolication, and automation of triage and debug workflows. Build and manage a world-class Panic Triage & Tools team, developing senior systems engineers into technical leaders Define and execute the multi-year technical roadmap for platform triage and reliability, partnering with senior cross-functional leaders to align with Apple's", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:6cd82b05c0e9d8f1c9f6bfd8e8ed4b86: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.c21788c501b7c413d3", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "6cd82b05c0e9d8f1c9f6bfd8e8ed4b86", "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 Engineering Manager, Triage Services and Infrastructure Cupertino, United States of America The Core OS team is seeking an exceptional engineering manager to lead the team responsible for enabling Apple's operating systems to achieve world-class reliability. This team develops and owns mission-critical tools and services that detect, analyze, and classify kernel panics and low-level crashes across all Apple platforms. You will be partnering with engineering teams across Software, Hardware, and Silicon groups to drive and deliver the rock-solid OS reliability for over 2 billion currently active Apple devices and shape the future of system reliability across Apple's entire product ecosystem. Lead a team of engineers triaging kernel panics and critical system-level issues across all Apple platforms (macOS, iOS, watchOS, tvOS). Build intelligent automation pipelines that analyze, group, and prioritize failure signatures based on their reliability impact. Mentor engineers to design and develop advanced systems diagnostic and at-scale debug services to realize the vision of zero-iteration debugging and fully automated triage and root cause analysis. Develop telemetry-based dashboards to monitor at-scale panic/crash triage and analysis services to ensure they are working as expected and efficiently. Collaborate with Core OS, Hardware, Silicon, and other engineering teams to champion and advance improvements in debuggability, panic data quality, symbolication, and automation of triage and debug workflows. Build and manage a world-class Panic Triage & Tools team, developing senior systems engineers into technical leaders Define and execute the multi-year technical roadmap for platform triage and reliability, partnering with senior cross-functional leaders to align with Apple's", "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": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200648961/software-engineering-manager-triage-services-and-infrastructure?team=SFTWR", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200648961/software-engineering-manager-triage-services-and-infrastructure?team=SFTWR", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200648961/software-engineering-manager-triage-services-and-infrastructure?team=SFTWR", "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/320193/000032019325000079/aapl-20250927.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.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.apple.com/en-us/details/200648961/software-engineering-manager-triage-services-and-infrastructure?team=SFTWR", "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.apple.com/en-us/details/200648961/software-engineering-manager-triage-services-and-infrastructure?team=SFTWR", "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.apple.com/en-us/details/200648961/software-engineering-manager-triage-services-and-infrastructure?team=SFTWR", "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": "ab97cb1a70d9e0818de928afbf7168f2", "title": "Engineer - Linux", "employer_name": "Cboe Global Markets", "employer_slug": "cboe-global-markets", "location_text": "Singapore", "country": "SG", "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": "2026-05-19T00:00:00.000Z", "apply_url": "https://cboe.wd1.myworkdayjobs.com/External_Career_CBOE/job/Singapore/Engineer---Linux_R-4417-1", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Engineer - Linux Singapore posted: Posted 24 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1374310/000162828026010013/cboe-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/1374310/000162828026010013/cboe-20251231.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "data", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Finance", "employer_industry_source": "source_sector", "employer_size": "5000+", "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.2c7a70f41d7411c119", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "ab97cb1a70d9e0818de928afbf7168f2", "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:ab97cb1a70d9e0818de928afbf7168f2: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/1374310/000162828026010013/cboe-20251231.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1374310/000162828026010013/cboe-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://cboe.wd1.myworkdayjobs.com/External_Career_CBOE/job/Singapore/Engineer---Linux_R-4417-1", "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://cboe.wd1.myworkdayjobs.com/External_Career_CBOE/job/Singapore/Engineer---Linux_R-4417-1", "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": "475e08c7a8f517c1b785823c1a60773c", "title": "Big Data Scientist", "employer_name": "Vodafone Group", "employer_slug": "vodafone-group", "location_text": "Athens, Attiki, Greece", "country": "GR", "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://vodafone.eightfold.ai/careers/job/563018697140634", "apply_url_verified": false, "ats": "eightfold", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Big Data Scientist Athens, Attiki, Greece", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "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.926a7aa29e6536a75d", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "475e08c7a8f517c1b785823c1a60773c", "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:475e08c7a8f517c1b785823c1a60773c: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://vodafone.eightfold.ai/careers/job/563018697140634", "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://vodafone.eightfold.ai/careers/job/563018697140634", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "80f444b8539c3a1d48a1be31f0ceff76", "title": "Quantitative Developer - HFT (C++)", "employer_name": "Qube Research & Technologies", "employer_slug": "qube-research-technologies", "location_text": "London", "country": "GB", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": 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": "2025-10-08T13:28:26.000Z", "apply_url": "https://job-boards.greenhouse.io/quberesearchandtechnologies/jobs/8203078002", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Quantitative Developer - HFT (C++) London Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT's collaborative mindset which enables us to solve the most complex challenges. QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors. QRT's High-Frequency Trading team designs and operates ultra-low-latency systems at global scale. We're hiring experienced C++ engineers who are passionate about performance, efficiency, and building highly optimized systems. You'll work on performance-critical systems with direct impact on live trading outcomes. Your future role at QRT: You'll work across our latency-critical stack - from kernel-level tuning and network optimization to highly efficient C++ components. This role involves close collaboration with our FPGA engineering team as well as our hardware and infrastructure engineers to design end-to-end architectures where every single nanosecond counts. - Design and optimize C++ systems for trading, market data, and infrastructure - Profile and fine-tune performance across CPU, cache, and memory layers - Collaborate with FPGA engineers to integrate hardware and software pathways Your present skillset: - 5+ years of experience in performance-critical C++ (C++17 or newer) - Strong grasp of systems programming, low-level understanding including memory management and CPU's architectures - Deep familiarity with Linux internals, kernel parameters, and low-level profiling - Curiosity about hardware, networking, and how systems", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "years_min": { "field": "years_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "years_experience_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "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": 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": 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.f01cfae4ddf39e09ae", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "80f444b8539c3a1d48a1be31f0ceff76", "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": "Quantitative Developer - HFT (C++) London Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT's collaborative mindset which enables us to solve the most complex challenges. QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors. QRT's High-Frequency Trading team designs and operates ultra-low-latency systems at global scale. We're hiring experienced C++ engineers who are passionate about performance, efficiency, and building highly optimized systems. You'll work on performance-critical systems with direct impact on live trading outcomes. Your future role at QRT: You'll work across our latency-critical stack - from kernel-level tuning and network optimization to highly efficient C++ components. This role involves close collaboration with our FPGA engineering team as well as our hardware and infrastructure engineers to design end-to-end architectures where every single nanosecond counts. - Design and optimize C++ systems for trading, market data, and infrastructure - Profile and fine-tune performance across CPU, cache, and memory layers - Collaborate with FPGA engineers to integrate hardware and software pathways Your present skillset: - 5+ years of experience in performance-critical C++ (C++17 or newer) - Strong grasp of systems programming, low-level understanding including memory management and CPU's architectures - Deep familiarity with Linux internals, kernel parameters, and low-level profiling - Curiosity about hardware, networking, and how systems", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:80f444b8539c3a1d48a1be31f0ceff76: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.5d5c697275694ab068", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "80f444b8539c3a1d48a1be31f0ceff76", "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": "Quantitative Developer - HFT (C++) London Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT's collaborative mindset which enables us to solve the most complex challenges. QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors. QRT's High-Frequency Trading team designs and operates ultra-low-latency systems at global scale. We're hiring experienced C++ engineers who are passionate about performance, efficiency, and building highly optimized systems. You'll work on performance-critical systems with direct impact on live trading outcomes. Your future role at QRT: You'll work across our latency-critical stack - from kernel-level tuning and network optimization to highly efficient C++ components. This role involves close collaboration with our FPGA engineering team as well as our hardware and infrastructure engineers to design end-to-end architectures where every single nanosecond counts. - Design and optimize C++ systems for trading, market data, and infrastructure - Profile and fine-tune performance across CPU, cache, and memory layers - Collaborate with FPGA engineers to integrate hardware and software pathways Your present skillset: - 5+ years of experience in performance-critical C++ (C++17 or newer) - Strong grasp of systems programming, low-level understanding including memory management and CPU's architectures - Deep familiarity with Linux internals, kernel parameters, and low-level profiling - Curiosity about hardware, networking, and how systems", "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": { "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/quberesearchandtechnologies/jobs/8203078002", "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/quberesearchandtechnologies/jobs/8203078002", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "8647a3558c189d2e467e2671dc0e8f47", "title": "Software Development Engineer - AI/ML, Amazon Neuron, Multimodal Inference", "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": "2026-05-08T00:00:00.000Z", "apply_url": "https://www.amazon.jobs/en/jobs/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-inference", "apply_url_verified": false, "ats": "amazon_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Development Engineer - AI/ML, Amazon Neuron, Multimodal Inference Seattle, Washington, USA The Annapurna Labs team at Amazonbuilds 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 at the intersection", "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": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "Industrial", "employer_industry_source": "wikidata_sparql_industry", "employer_size": "5000+", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": true, "us_citizenship_required": false, "security_clearance": "none", "education_required": "bachelors", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.6347deed7120302e48", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "8647a3558c189d2e467e2671dc0e8f47", "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, Amazon Neuron, Multimodal Inference Seattle, Washington, USA The Annapurna Labs team at Amazonbuilds 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 at the intersection", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:8647a3558c189d2e467e2671dc0e8f47: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.3f0235204ea1e163fc", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "8647a3558c189d2e467e2671dc0e8f47", "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, Amazon Neuron, Multimodal Inference Seattle, Washington, USA The Annapurna Labs team at Amazonbuilds 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 at the intersection", "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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-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/10415417/software-development-engineer-ai-ml-amazon-neuron-multimodal-inference", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-07T00:00:00Z" } } }, { "id": "8e83ceb8837c8be0ca5a3c78a9b8af43", "title": "Linux Systems Administrator", "employer_name": "CACI", "employer_slug": "caci", "location_text": "2 Locations", "country": "unknown", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [], "posted_at": "2026-06-04T00:00:00.000Z", "apply_url": "https://caci.wd1.myworkdayjobs.com/External/job/Chantilly-VA-US/Linux-Systems-Administrator_327327", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Linux Systems Administrator 2 Locations posted: Posted 8 Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/16058/000162828025038739/caci-20250630.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/16058/000162828025038739/caci-20250630.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "other", "role_function_source": "unknown", "company_stage": null, "company_stage_source": null, "employer_industry": 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.a1eaf40a00279bd2c9", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "8e83ceb8837c8be0ca5a3c78a9b8af43", "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:8e83ceb8837c8be0ca5a3c78a9b8af43: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/16058/000162828025038739/caci-20250630.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/16058/000162828025038739/caci-20250630.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://caci.wd1.myworkdayjobs.com/External/job/Chantilly-VA-US/Linux-Systems-Administrator_327327", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null } } }, { "id": "93723d22d8c161ff74092de889333669", "title": "Software Engineer, Core Networking", "employer_name": "Apple", "employer_slug": "apple", "location_text": "San Francisco Bay Area, United States of America", "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": 147400, "salary_max": 272100, "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": 147400, "base_salary_max": 272100, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": 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": "2026-01-21T23:23:36.786Z", "apply_url": "https://jobs.apple.com/en-us/details/200618355/software-engineer-core-networking?team=SFTWR", "apply_url_verified": false, "ats": "apple_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Software Engineer, Core Networking San Francisco Bay Area, United States of America The Core Networking team is responsible for developing, deploying and operating Apple's networking software. Whether it's TCP/IP networking stack software that powers billions of iPhones, or high performance kernel bypass and hardware offload running on Apple Silicon servers powering Private Cloud Compute, our work covers the entire network lifecycle, including silicon/hardware design, driver and runtime. The candidate needs to be able to work in a fast paced environment, operate gracefully under stress, handle multiple assignments in parallel, and be self driven to be effective in an environment with little overhead. The candidate also needs to show initiative, be able to take ownership of a problem area, and demonstrate an ability to understand the role of the group's technology in the larger context of the product and network environment. The position has cross-functional aspects and the candidate will need to show excellent interpersonal and communication skills. Design and implement core networking functionality that enables high performance networking, including DPDK, OpenvSwitch and RDMA. Collaborate with Apple Silicon, hardware engineering and data center teams, and/or external partners in building scalable networking infrastructure. Work with cross functional teams through software releases to production, troubleshooting and monitoring. Minimum Qualifications: BS in Computer Science, Electrical Engineering or equivalent networking software development experience Skill in C/C++, software build toolchain and utilities Knowledge and experience with TCP/IP networking Preferred Qualifications: Experience in advanced networking architecture (DPDK, RDMA, HW offload, network virtualization) Experience in RDMA/RoCE networks Experience", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "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" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "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/320193/000032019325000079/aapl-20250927.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_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": "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": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.query.description_excerpt.70d96645a7bf5a52a7", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "93723d22d8c161ff74092de889333669", "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, Core Networking San Francisco Bay Area, United States of America The Core Networking team is responsible for developing, deploying and operating Apple's networking software. Whether it's TCP/IP networking stack software that powers billions of iPhones, or high performance kernel bypass and hardware offload running on Apple Silicon servers powering Private Cloud Compute, our work covers the entire network lifecycle, including silicon/hardware design, driver and runtime. The candidate needs to be able to work in a fast paced environment, operate gracefully under stress, handle multiple assignments in parallel, and be self driven to be effective in an environment with little overhead. The candidate also needs to show initiative, be able to take ownership of a problem area, and demonstrate an ability to understand the role of the group's technology in the larger context of the product and network environment. The position has cross-functional aspects and the candidate will need to show excellent interpersonal and communication skills. Design and implement core networking functionality that enables high performance networking, including DPDK, OpenvSwitch and RDMA. Collaborate with Apple Silicon, hardware engineering and data center teams, and/or external partners in building scalable networking infrastructure. Work with cross functional teams through software releases to production, troubleshooting and monitoring. Minimum Qualifications: BS in Computer Science, Electrical Engineering or equivalent networking software development experience Skill in C/C++, software build toolchain and utilities Knowledge and experience with TCP/IP networking Preferred Qualifications: Experience in advanced networking architecture (DPDK, RDMA, HW offload, network virtualization) Experience in RDMA/RoCE networks Experience", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:93723d22d8c161ff74092de889333669: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.7eb2ea3fb7810950dc", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "93723d22d8c161ff74092de889333669", "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, Core Networking San Francisco Bay Area, United States of America The Core Networking team is responsible for developing, deploying and operating Apple's networking software. Whether it's TCP/IP networking stack software that powers billions of iPhones, or high performance kernel bypass and hardware offload running on Apple Silicon servers powering Private Cloud Compute, our work covers the entire network lifecycle, including silicon/hardware design, driver and runtime. The candidate needs to be able to work in a fast paced environment, operate gracefully under stress, handle multiple assignments in parallel, and be self driven to be effective in an environment with little overhead. The candidate also needs to show initiative, be able to take ownership of a problem area, and demonstrate an ability to understand the role of the group's technology in the larger context of the product and network environment. The position has cross-functional aspects and the candidate will need to show excellent interpersonal and communication skills. Design and implement core networking functionality that enables high performance networking, including DPDK, OpenvSwitch and RDMA. Collaborate with Apple Silicon, hardware engineering and data center teams, and/or external partners in building scalable networking infrastructure. Work with cross functional teams through software releases to production, troubleshooting and monitoring. Minimum Qualifications: BS in Computer Science, Electrical Engineering or equivalent networking software development experience Skill in C/C++, software build toolchain and utilities Knowledge and experience with TCP/IP networking Preferred Qualifications: Experience in advanced networking architecture (DPDK, RDMA, HW offload, network virtualization) Experience in RDMA/RoCE networks Experience", "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": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200618355/software-engineer-core-networking?team=SFTWR", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200618355/software-engineer-core-networking?team=SFTWR", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200618355/software-engineer-core-networking?team=SFTWR", "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/320193/000032019325000079/aapl-20250927.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.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.apple.com/en-us/details/200618355/software-engineer-core-networking?team=SFTWR", "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.apple.com/en-us/details/200618355/software-engineer-core-networking?team=SFTWR", "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.apple.com/en-us/details/200618355/software-engineer-core-networking?team=SFTWR", "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": "6eeb31d2f6397a871e074e099d3245f8", "title": "Senior Machine Learning Software Engineer", "employer_name": "Autodesk", "employer_slug": "autodesk", "location_text": "Kraków, POL", "country": "PL", "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": "2026-06-12T00:00:00.000Z", "apply_url": "https://autodesk.wd1.myworkdayjobs.com/Ext/job/Krakw-POL/Senior-Machine-Learning-Software-Engineer_26WD98709-2", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Senior Machine Learning Software Engineer Kraków, POL posted: Posted Today", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 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/769397/000076939726000015/adsk-20260131.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule:title_override", "db_column": "role_function" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/769397/000076939726000015/adsk-20260131.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "senior", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 5, "years_experience_max": 9, "role_function": "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": 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.ec11c5b97135262c64", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "6eeb31d2f6397a871e074e099d3245f8", "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:6eeb31d2f6397a871e074e099d3245f8: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/769397/000076939726000015/adsk-20260131.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/769397/000076939726000015/adsk-20260131.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://autodesk.wd1.myworkdayjobs.com/Ext/job/Krakw-POL/Senior-Machine-Learning-Software-Engineer_26WD98709-2", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": null }, "role_function": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://autodesk.wd1.myworkdayjobs.com/Ext/job/Krakw-POL/Senior-Machine-Learning-Software-Engineer_26WD98709-2", "source_values": [ "rule:title_override" ], "checked_at": null } } }, { "id": "776097b1bbb887e627ec6725edca7e50", "title": "Machine Learning Engineer - Humanoid Robotics", "employer_name": "NVIDIA Corporation", "employer_slug": "nvidia", "location_text": "China, Shanghai", "country": "CN", "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://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/China-Shanghai/Machine-Learning-Engineer---Humanoid-Robotics_JR2016809", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Machine Learning Engineer - Humanoid Robotics China, Shanghai posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1045810/000104581026000021/nvda-20260125.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/1045810/000104581026000021/nvda-20260125.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.6cfc9ae3ac6339e743", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "776097b1bbb887e627ec6725edca7e50", "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:776097b1bbb887e627ec6725edca7e50: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/1045810/000104581026000021/nvda-20260125.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1045810/000104581026000021/nvda-20260125.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://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/China-Shanghai/Machine-Learning-Engineer---Humanoid-Robotics_JR2016809", "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://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/China-Shanghai/Machine-Learning-Engineer---Humanoid-Robotics_JR2016809", "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": "b68f3623b889efc81132a5b72306de62", "title": "Manufacturing Linux Network Engineer", "employer_name": "Cerebras Systems", "employer_slug": "cerebras-systems", "location_text": "Sunnyvale, CA", "country": "US", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "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": [ "cbinsights_unicorn" ], "posted_at": "2026-05-01T16:11:29.000Z", "apply_url": "https://job-boards.greenhouse.io/cerebrassystems/jobs/7629637003", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Manufacturing Linux Network Engineer Sunnyvale, CA Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. About The Role We are seeking an experienced Manufacturing Linux / Network Engineer to design, implement, and maintain robust IT and network infrastructure across our manufacturing facilities. The ideal candidate brings deep expertise in Linux systems administration (Red Hat / Rocky Linux), network security (Palo Alto firewalls), storage infrastructure, CI/CD pipelines (Jenkins), and infrastructure automation (Ansible). This role sits at the intersection of enterprise IT and plant-floor operations, and is critical to delivering the high availability, security, and performance that modern manufacturing environments demand. Responsibilities -", "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" }, "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" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 4, "years_experience_max": 8, "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": true, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": 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.semantic.role_function.dcab266f79172020e3", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "b68f3623b889efc81132a5b72306de62", "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:b68f3623b889efc81132a5b72306de62: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": "2026-06-20T05:42:05.724Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/cerebrassystems/jobs/7629637003", "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/cerebrassystems/jobs/7629637003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "9603805ed1c42d62bc3d87e645a12071", "title": "Runtime Engineer", "employer_name": "MatX", "employer_slug": "matx", "location_text": "Mountain View, CA", "country": "US", "employment_type": "full_time", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 120000, "salary_max": 250000, "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": 120000, "base_salary_max": 250000, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": "yes", "match_401k_pct": 5, "match_401k_pct_source": "llm:greenfield:inv_371_postfix", "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": true, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn" ], "posted_at": "2026-05-27T03:06:08.000Z", "apply_url": "https://job-boards.greenhouse.io/matx/jobs/5231974008", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Runtime Engineer Mountain View, CA What MatX is Building MatX is building custom silicon for large-language-model inference and training, with HW/SW co-design across ISA, RTL, simulator, compiler, and kernels so each layer benefits from the others. The runtime owns the host-side stack and the contracts that bind those teams together. What You'll Do Here - Build the host-side interface library - device memory management, DMA, streams and events, sync primitives - that every compiler-emitted program runs on top of - Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently - Design the custom-kernel ABI - calling convention, sync semantics, lifecycle - and the host-side marshaling layer (DLPack, the buffer protocol, numpy) that gets Python tensors to the device - Build Python bindings via PyO3, with a C-ABI shim as the alternative integration path for downstream consumers - Build the LLM inference serving stack - paged KV cache, continuous batching, request scheduling, token streaming - and the cluster orchestration primitives underneath it - Bring up interconnect topology from the host and own the failure-detection and clean-teardown path for stop-restructure-resume recovery across racks - Design what the chip exposes to host-side profilers and debuggers - perf counters, traces, and the Python surfaces ML engineers actually use - and hit measurable performance targets on runtime overhead and serving throughput Who You Are - Strong experience in a systems programming language - Rust, C, C++, or Go - including memory", "parental_leave_weeks": 12, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "field": "k401_match", "source": "llm:greenfield:inv_371_postfix", "db_column": "k401_match" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "match_401k_pct": { "field": "match_401k_pct", "source": "llm:greenfield:inv_371_postfix", "db_column": "match_401k_pct" }, "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": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" }, "visible_salary_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": "llm:greenfield:inv_371_postfix", "db_column": "parental_leave_weeks" } }, "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": "source_sector", "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": 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.01011d2d229dc1ea82", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9603805ed1c42d62bc3d87e645a12071", "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": "Runtime Engineer Mountain View, CA What MatX is Building MatX is building custom silicon for large-language-model inference and training, with HW/SW co-design across ISA, RTL, simulator, compiler, and kernels so each layer benefits from the others. The runtime owns the host-side stack and the contracts that bind those teams together. What You'll Do Here - Build the host-side interface library - device memory management, DMA, streams and events, sync primitives - that every compiler-emitted program runs on top of - Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently - Design the custom-kernel ABI - calling convention, sync semantics, lifecycle - and the host-side marshaling layer (DLPack, the buffer protocol, numpy) that gets Python tensors to the device - Build Python bindings via PyO3, with a C-ABI shim as the alternative integration path for downstream consumers - Build the LLM inference serving stack - paged KV cache, continuous batching, request scheduling, token streaming - and the cluster orchestration primitives underneath it - Bring up interconnect topology from the host and own the failure-detection and clean-teardown path for stop-restructure-resume recovery across racks - Design what the chip exposes to host-side profilers and debuggers - perf counters, traces, and the Python surfaces ML engineers actually use - and hit measurable performance targets on runtime overhead and serving throughput Who You Are - Strong experience in a systems programming language - Rust, C, C++, or Go - including memory", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:9603805ed1c42d62bc3d87e645a12071: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.234de7af0965a5c36f", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9603805ed1c42d62bc3d87e645a12071", "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": "Runtime Engineer Mountain View, CA What MatX is Building MatX is building custom silicon for large-language-model inference and training, with HW/SW co-design across ISA, RTL, simulator, compiler, and kernels so each layer benefits from the others. The runtime owns the host-side stack and the contracts that bind those teams together. What You'll Do Here - Build the host-side interface library - device memory management, DMA, streams and events, sync primitives - that every compiler-emitted program runs on top of - Own and extend the executable format: the compiler→runtime contract, its versioning, the weight and quantization layouts that let compiler and runtime evolve independently - Design the custom-kernel ABI - calling convention, sync semantics, lifecycle - and the host-side marshaling layer (DLPack, the buffer protocol, numpy) that gets Python tensors to the device - Build Python bindings via PyO3, with a C-ABI shim as the alternative integration path for downstream consumers - Build the LLM inference serving stack - paged KV cache, continuous batching, request scheduling, token streaming - and the cluster orchestration primitives underneath it - Bring up interconnect topology from the host and own the failure-detection and clean-teardown path for stop-restructure-resume recovery across racks - Design what the chip exposes to host-side profilers and debuggers - perf counters, traces, and the Python surfaces ML engineers actually use - and hit measurable performance targets on runtime overhead and serving throughput Who You Are - Strong experience in a systems programming language - Rust, C, C++, or Go - including memory", "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/matx/jobs/5231974008", "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/matx/jobs/5231974008", "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/matx/jobs/5231974008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/matx/jobs/5231974008", "source_values": [ "derived:greenfield:inv_371_postfix" ], "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://job-boards.greenhouse.io/matx/jobs/5231974008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "match_401k_pct": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/matx/jobs/5231974008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "k401_match": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/matx/jobs/5231974008", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/matx/jobs/5231974008", "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/matx/jobs/5231974008", "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": "96f17cab1d11f8c7fffa556afe7f504a", "title": "OS Performance Engineer, CoreOS", "employer_name": "Apple", "employer_slug": "apple", "location_text": "Cupertino, United States of America", "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": 147400, "salary_max": 272100, "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": 147400, "base_salary_max": 272100, "salary_disclosed": true, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options", "espp" ], "k401_match": "yes", "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "k401_contribution_source_url": null, "k401_contribution_source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "profit_sharing": false, "bonus_offered": 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": "2025-10-20T22:27:49.719Z", "apply_url": "https://jobs.apple.com/en-us/details/200624838/os-performance-engineer-coreos?team=SFTWR", "apply_url_verified": false, "ats": "apple_custom", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "OS Performance Engineer, CoreOS Cupertino, United States of America The OS Performance team at Apple is seeking an exceptional Systems Engineer to drive performance excellence across Apple's software and hardware ecosystem. This role involves working across the full software stack-from high-level applications and frameworks to low-level kernel components-ensuring that Apple delivers world-class performance on supported products. The position presents diverse challenges spanning from optimizing performance on currently supported hardware to unleashing the full potential of cutting-edge next-generation products. As a Systems Engineer on this team, you will have the opportunity to make a direct impact on the user experience for millions of customers worldwide by ensuring every device, regardless of generation, delivers the smooth, responsive experience Apple customers expect. In this role, you will dive deep into performance issues surfaced across Apple's ecosystem and will be working with cross-functional partner teams from various apps and frameworks to decipher complex system conditions that impact performance and develop solutions to resolve them. You'll have the opportunity to drive innovative performance optimization ideas from conception to delivery, develop measurement tools and analysis frameworks, and create testing methodologies that reflect real-world usage. The ideal candidate will be someone who is self-motivated and enjoys debugging complex systems, with a natural curiosity for understanding how different components interact and impact overall performance. Analyze and fix challenging performance problems in software for various user facing workloads in order to provide the best-in-class experience on Apple products. Engage with other engineering teams from firmware, kernel, applications and hardware", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 44, "benefit_verified": true, "benefit_last_verified": "2026-06-13T01:18:42.546Z", "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" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_min" }, "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/320193/000032019325000079/aapl-20250927.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" }, "equity_included": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.htm", "source_fields": [ "equity_offered", "equity_type" ] }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" } }, "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": "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.5ebfa702efcfb63bff", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "96f17cab1d11f8c7fffa556afe7f504a", "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": "OS Performance Engineer, CoreOS Cupertino, United States of America The OS Performance team at Apple is seeking an exceptional Systems Engineer to drive performance excellence across Apple's software and hardware ecosystem. This role involves working across the full software stack-from high-level applications and frameworks to low-level kernel components-ensuring that Apple delivers world-class performance on supported products. The position presents diverse challenges spanning from optimizing performance on currently supported hardware to unleashing the full potential of cutting-edge next-generation products. As a Systems Engineer on this team, you will have the opportunity to make a direct impact on the user experience for millions of customers worldwide by ensuring every device, regardless of generation, delivers the smooth, responsive experience Apple customers expect. In this role, you will dive deep into performance issues surfaced across Apple's ecosystem and will be working with cross-functional partner teams from various apps and frameworks to decipher complex system conditions that impact performance and develop solutions to resolve them. You'll have the opportunity to drive innovative performance optimization ideas from conception to delivery, develop measurement tools and analysis frameworks, and create testing methodologies that reflect real-world usage. The ideal candidate will be someone who is self-motivated and enjoys debugging complex systems, with a natural curiosity for understanding how different components interact and impact overall performance. Analyze and fix challenging performance problems in software for various user facing workloads in order to provide the best-in-class experience on Apple products. Engage with other engineering teams from firmware, kernel, applications and hardware", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:96f17cab1d11f8c7fffa556afe7f504a: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.a53b6a5b2403aae771", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "96f17cab1d11f8c7fffa556afe7f504a", "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": "OS Performance Engineer, CoreOS Cupertino, United States of America The OS Performance team at Apple is seeking an exceptional Systems Engineer to drive performance excellence across Apple's software and hardware ecosystem. This role involves working across the full software stack-from high-level applications and frameworks to low-level kernel components-ensuring that Apple delivers world-class performance on supported products. The position presents diverse challenges spanning from optimizing performance on currently supported hardware to unleashing the full potential of cutting-edge next-generation products. As a Systems Engineer on this team, you will have the opportunity to make a direct impact on the user experience for millions of customers worldwide by ensuring every device, regardless of generation, delivers the smooth, responsive experience Apple customers expect. In this role, you will dive deep into performance issues surfaced across Apple's ecosystem and will be working with cross-functional partner teams from various apps and frameworks to decipher complex system conditions that impact performance and develop solutions to resolve them. You'll have the opportunity to drive innovative performance optimization ideas from conception to delivery, develop measurement tools and analysis frameworks, and create testing methodologies that reflect real-world usage. The ideal candidate will be someone who is self-motivated and enjoys debugging complex systems, with a natural curiosity for understanding how different components interact and impact overall performance. Analyze and fix challenging performance problems in software for various user facing workloads in order to provide the best-in-class experience on Apple products. Engage with other engineering teams from firmware, kernel, applications and hardware", "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": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200624838/os-performance-engineer-coreos?team=SFTWR", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_max": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200624838/os-performance-engineer-coreos?team=SFTWR", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "salary_type": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.apple.com/en-us/details/200624838/os-performance-engineer-coreos?team=SFTWR", "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/320193/000032019325000079/aapl-20250927.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/320193/000032019325000079/aapl-20250927.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.apple.com/en-us/details/200624838/os-performance-engineer-coreos?team=SFTWR", "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.apple.com/en-us/details/200624838/os-performance-engineer-coreos?team=SFTWR", "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": "fdfd4b3f1d80adf8e5154bfd02187e4f", "title": "Unix AIX Engineer", "employer_name": "DXC Technology", "employer_slug": "dxc-technology", "location_text": "GBR - LAN - PRESTON", "country": "GB", "employment_type": "unknown", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 3, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "rsu", "options" ], "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://dxctechnology.wd1.myworkdayjobs.com/DXCJobs/job/GBR---LAN---PRESTON/Unix-AIX-Engineer_51563747", "apply_url_verified": false, "ats": "workday", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Unix AIX Engineer GBR - LAN - PRESTON posted: Posted 30+ Days Ago", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "equity_type": { "source": "sec_10k", "source_url": "https://www.sec.gov/Archives/edgar/data/1688568/000168856826000022/dxc-20260331.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/1688568/000168856826000022/dxc-20260331.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": "source_sector", "employer_size": null, "quality_score": 35, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": false, "us_citizenship_required": false, "security_clearance": "none", "education_required": "none", "roll_up_count": null, "roll_up_total_locations": null, "explanations": [ { "schema_version": "inv382.match_signal.v1", "signal_id": "sig.semantic.role_function.f68b62d489ced941ca", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "fdfd4b3f1d80adf8e5154bfd02187e4f", "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:fdfd4b3f1d80adf8e5154bfd02187e4f: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/1688568/000168856826000022/dxc-20260331.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/1688568/000168856826000022/dxc-20260331.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://dxctechnology.wd1.myworkdayjobs.com/DXCJobs/job/GBR---LAN---PRESTON/Unix-AIX-Engineer_51563747", "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://dxctechnology.wd1.myworkdayjobs.com/DXCJobs/job/GBR---LAN---PRESTON/Unix-AIX-Engineer_51563747", "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": "9a49efc6fbe13b3cd7b1d28d3e0fc3ce", "title": "CoDesign & NextGen - New College Grad", "employer_name": "Cerebras Systems", "employer_slug": "cerebras-systems", "location_text": "Sunnyvale, CA", "country": "US", "employment_type": "full_time", "remote_status": "unspecified", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": null, "timezone_overlap_hours": 6, "salary_min": 145000, "salary_max": 155000, "salary_min_source": null, "salary_max_source": null, "salary_currency": "USD", "salary_type": "base", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": "year", "base_salary_min": 145000, "base_salary_max": 155000, "salary_disclosed": true, "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": [ "cbinsights_unicorn" ], "posted_at": "2026-01-07T04:02:02.000Z", "apply_url": "https://job-boards.greenhouse.io/cerebrassystems/jobs/7580399003", "apply_url_verified": false, "ats": "greenhouse", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "CoDesign & NextGen - New College Grad Sunnyvale, CA Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. About The Role Engineers in the CoDesign and NextGen organization work at the interface of software and hardware helping everything from high performance kernel design, next generation ASIC performance modeling and tuning, new system bringup and software tuning, system robustness and simulations. As an new engineer you'll be expected to learn the Cerebras products and platforms end to end starting at the base layer of programming the Wafer Scale Engine through kernel development, modeling performance for our software products and validating the work", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 47, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_max": { "field": "salary_max", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_max" }, "salary_min": { "field": "salary_min", "source": "llm:greenfield:inv_371_postfix", "db_column": "base_salary_min" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "company_stage": { "field": "company_stage", "source": "llm:greenfield:inv_371_postfix", "db_column": "company_stage" }, "customer_type": { "field": "customer_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "customer_type" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "salary_period": { "field": "salary_period", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_period" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "employment_type": { "field": "employment_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "employment_type" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "salary_currency": { "field": "salary_currency", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_currency" }, "salary_disclosed": { "field": "salary_disclosed", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_disclosed" }, "visa_sponsorship": { "field": "visa_sponsorship", "source": "llm:greenfield:inv_371_postfix", "db_column": "visa_sponsorship" }, "industry_vertical": { "field": "industry_vertical", "source": "llm:greenfield:inv_371_postfix", "db_column": "industry_vertical" }, "naics_sector_code": { "field": "naics_sector_code", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_code" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" }, "naics_sector_title": { "field": "naics_sector_title", "source": "llm:greenfield:inv_371_postfix", "db_column": "naics_sector_title" }, "visible_salary_max": { "field": "visible_salary_max", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_max" }, "visible_salary_min": { "field": "visible_salary_min", "source": "derived:greenfield:inv_371_postfix", "db_column": "salary_min" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "engineering", "role_function_source": "llm:greenfield:inv_371_postfix", "company_stage": "public-company", "company_stage_source": "llm:greenfield:inv_371_postfix", "employer_industry": null, "employer_industry_source": null, "employer_size": "51-200", "quality_score": 55, "travel_pct": null, "requires_shift": null, "shift_type": "unknown", "shift_type_source": null, "weekend_work": "unknown", "weekend_work_source": null, "relocation_assistance_source": null, "contractor_w2": "unknown", "required_timezone": null, "fertility_family_building_benefits_source": null, "mental_health_support_source": null, "adoption_assistance_offered_source": null, "childcare_subsidy_source": null, "learning_budget_offered_source": null, "surrogacy_assistance_offered_source": null, "cover_letter_required": null, "cover_letter_required_source": null, "assessment_required": null, "assessment_required_source": null, "application_deadline": null, "application_deadline_source": null, "visa_sponsorship": 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.1bbd2eb58391aa3988", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9a49efc6fbe13b3cd7b1d28d3e0fc3ce", "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": "CoDesign & NextGen - New College Grad Sunnyvale, CA Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. About The Role Engineers in the CoDesign and NextGen organization work at the interface of software and hardware helping everything from high performance kernel design, next generation ASIC performance modeling and tuning, new system bringup and software tuning, system robustness and simulations. As an new engineer you'll be expected to learn the Cerebras products and platforms end to end starting at the base layer of programming the Wafer Scale Engine through kernel development, modeling performance for our software products and validating the work", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:9a49efc6fbe13b3cd7b1d28d3e0fc3ce: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.213ce52aa6ea1ea042", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9a49efc6fbe13b3cd7b1d28d3e0fc3ce", "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": "CoDesign & NextGen - New College Grad Sunnyvale, CA Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. About The Role Engineers in the CoDesign and NextGen organization work at the interface of software and hardware helping everything from high performance kernel design, next generation ASIC performance modeling and tuning, new system bringup and software tuning, system robustness and simulations. As an new engineer you'll be expected to learn the Cerebras products and platforms end to end starting at the base layer of programming the Wafer Scale Engine through kernel development, modeling performance for our software products and validating the 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": null, "k401_match_source_label": "DOL Form 5500 filing", "k401_contribution_source_label": "DOL Form 5500 filing", "apply_url_checked_at": null, "apply_url_verification_status": "not_reachable_or_not_checked", "apply_url_verification_label": "Not checked or not reachable at last crawl", "field_source_signals": { "salary_min": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/cerebrassystems/jobs/7580399003", "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/cerebrassystems/jobs/7580399003", "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/cerebrassystems/jobs/7580399003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/cerebrassystems/jobs/7580399003", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "k401_match": { "tier": "government", "verified": true, "label": "Verified - DOL Form 5500", "source_label": "DOL Form 5500", "href": null, "source_values": [ "dol_form_5500", "EMPLR_CONTRIB_INCOME_AMT" ], "checked_at": "2026-06-07T00:00:00Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/cerebrassystems/jobs/7580399003", "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/cerebrassystems/jobs/7580399003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "company_stage": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://job-boards.greenhouse.io/cerebrassystems/jobs/7580399003", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } }, { "id": "914cc80c6bfea4597e1af0b34197e9cd", "title": "DevOps Service Engineer - MCCM", "employer_name": "Vodafone Group", "employer_slug": "vodafone-group", "location_text": "Ankara, Ankara, Türkiye", "country": "TR", "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://vodafone.eightfold.ai/careers/job/563018696546238", "apply_url_verified": false, "ats": "eightfold", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "DevOps Service Engineer - MCCM Ankara, Ankara, Türkiye", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 40, "benefit_verified": true, "benefit_last_verified": "2026-06-07T00:00:00Z", "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": null, "llm_extraction_run_id": null, "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "k401_match": { "source": "dol_form_5500", "source_url": null, "source_fields": [ "EMPLR_CONTRIB_INCOME_AMT" ], "source_label": "DOL Form 5500 filing" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "role_function": { "field": "role_function", "source": "rule", "db_column": "role_function" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "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.60a5d5a6c93e2ae2b9", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "914cc80c6bfea4597e1af0b34197e9cd", "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:914cc80c6bfea4597e1af0b34197e9cd: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://vodafone.eightfold.ai/careers/job/563018696546238", "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://vodafone.eightfold.ai/careers/job/563018696546238", "source_values": [ "rule" ], "checked_at": null } } }, { "id": "a1e0509a9883eec6761ef30cf9a44a6f", "title": "Corporate", "employer_name": "Automatic Data Processing", "employer_slug": "automatic-data-processing", "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": 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": "2026-06-04T16:41:40.000Z", "apply_url": "https://jobs.adp.com/en/jobs/?orderby=0&pagesize=20&page=1&radius=100&rType=0&team=Digital%20Marketing&team=Management&team=Training&team=Administrative&team=Finance&team=Legal&team=Marketing&team=Operations", "apply_url_verified": false, "ats": "custom_structured", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Corporate Corporate", "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/8670/000000867025000037/adp-20250630.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/8670/000000867025000037/adp-20250630.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": "finance", "role_function_source": "rule", "company_stage": null, "company_stage_source": null, "employer_industry": "Professional Services", "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.dc2061624ed270c037", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "a1e0509a9883eec6761ef30cf9a44a6f", "signal_type": "query_match", "display_text": "Role: semantic match", "tooltip": "Role provided semantic evidence for your search intent.", "source_binding": "vector_semantic_evidence", "source_field": "role_function", "source_value": "finance", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:a1e0509a9883eec6761ef30cf9a44a6f: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/8670/000000867025000037/adp-20250630.htm", "source_values": [ "sec_10k", "https://www.sec.gov/Archives/edgar/data/8670/000000867025000037/adp-20250630.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://jobs.adp.com/en/jobs/?orderby=0&pagesize=20&page=1&radius=100&rType=0&team=Digital%20Marketing&team=Management&team=Training&team=Administrative&team=Finance&team=Legal&team=Marketing&team=Operations", "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://jobs.adp.com/en/jobs/?orderby=0&pagesize=20&page=1&radius=100&rType=0&team=Digital%20Marketing&team=Management&team=Training&team=Administrative&team=Finance&team=Legal&team=Marketing&team=Operations", "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": "9a944f5cd5adec5c1d0d1067adbf4b0e", "title": "Inference Performance Engineer", "employer_name": "Material Depot", "employer_slug": "material-depot", "location_text": "New York, NY | Hybrid | Remote", "country": "US", "employment_type": "unknown", "remote_status": "hybrid", "remote_status_source": null, "remote_status_enriched_at": null, "days_in_office": 2, "timezone_overlap_hours": 6, "salary_min": null, "salary_max": null, "salary_min_source": null, "salary_max_source": null, "salary_currency": "UNSPECIFIED", "salary_type": "unknown", "salary_type_source": null, "salary_type_enriched_at": null, "salary_period": null, "base_salary_min": null, "base_salary_max": null, "salary_disclosed": false, "equity_included": true, "equity_included_source": null, "equity_type": [ "options" ], "k401_match": null, "match_401k_pct": null, "match_401k_pct_source": null, "k401_match_source_url": null, "k401_match_source_fields": [], "k401_contribution_source_url": null, "k401_contribution_source_fields": [], "profit_sharing": false, "bonus_offered": false, "mental_health_support": null, "childcare_subsidy": null, "fertility_family_building_benefits": null, "adoption_assistance_offered": null, "surrogacy_assistance_offered": null, "student_loan_repayment_offered": null, "learning_budget_offered": null, "relocation_assistance": null, "top_startup_sources": [ "cbinsights_unicorn", "a16z" ], "posted_at": "2026-06-10T03:52:15.000Z", "apply_url": "https://jobs.ashbyhq.com/material/fb4b5605-9a2a-4e74-b4a9-80a03ee921db", "apply_url_verified": false, "ats": "ashby", "url_last_checked_alive": null, "removed_at": null, "description_excerpt": "Inference Performance Engineer New York, NY | Hybrid | Remote About the role Serving frontier models at scale requires solving novel systems problems at every layer of the stack. As an Inference Performance Engineer, you'll own the runtime that turns accelerators into a production serving system, optimizing throughput, latency, and cost across thousands of nodes. You'll work alongside hardware and compiler teams operating at the frontier of AI silicon design. What you'll do - Build and improve the inference runtime - Design scheduling, continuous batching, KV cache, and prefill/decode disaggregation - Implement low-precision kernels and speculative decoding - Drive throughput, latency, and cost per token - Collaborate with hardware teams on kernels, operators, and graph optimizations - Own the OpenAI-compatible API surface and serving protocol - Build benchmarking, profiling, and regression infrastructure What you'll need - BS in CS, EE, or related field, or equivalent experience - Software engineering experience: Rust, Go, Python, or C++ - Understanding of concurrency, memory, and tail latency - Understanding of modern inference: transformers, attention, KV cache, batching, speculative decoding, quantization - Experience with model serving frameworks: vLLM, TGI, SGLang, TensorRT-LLM, llama.cpp, or custom runtimes - GPU or ASIC programming experience: CUDA, ROCm, Triton, or vendor-native toolchains - Experience with low-precision inference (FP8, FP4, INT4) - Profiling and benchmarking experience: Nsight, perf, custom harnesses What we offer - Top-tier compensation structured to recognize and retain the best talent - Meaningful equity - Comprehensive medical, dental, vision, life, and disability insurance - Parental leave for all", "parental_leave_weeks": null, "non_birth_parent_leave_weeks": null, "parental_leave_weeks_source": null, "non_birth_parent_leave_weeks_source": null, "parental_leave_source_url": null, "pto_days": null, "unlimited_pto": false, "sabbatical_eligible": false, "family_friendly_score": 43, "benefit_verified": false, "benefit_last_verified": null, "hire_states_allowed": [], "state_eligibility_source": null, "state_eligibility_enriched_at": null, "llm_quality_pass_at": null, "llm_signals_at": "2026-06-20T05:42:05.724Z", "llm_extraction_run_id": "postfix_final_004037", "llm_extraction_extracted_at": null, "llm_extraction_field_sources": { "seniority": { "field": "seniority", "source": "llm:greenfield:inv_371_postfix", "db_column": "seniority" }, "tech_stack": { "field": "tech_stack", "source": "llm:greenfield:inv_371_postfix", "db_column": "tech_stack" }, "role_family": { "field": "role_family", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_family" }, "salary_type": { "field": "salary_type", "source": "llm:greenfield:inv_371_postfix", "db_column": "salary_type" }, "remote_status": { "field": "remote_status", "source": "rule:remote_status", "db_column": "remote_status" }, "role_function": { "field": "role_function", "source": "llm:greenfield:inv_371_postfix", "db_column": "role_function" }, "days_in_office": { "field": "days_in_office", "source": "rule:remote_status", "db_column": "days_in_office" }, "equity_offered": { "field": "equity_offered", "source": "llm:greenfield:inv_371_postfix", "db_column": "equity_offered" }, "equity_included": { "field": "equity_included", "source": "derived:greenfield:inv_371_postfix", "db_column": "equity_included" }, "education_required": { "field": "education_required", "source": "llm:greenfield:inv_371_postfix", "db_column": "education_required" } }, "seniority": "mid", "seniority_source": "llm:greenfield:inv_371_postfix", "years_experience_min": 3, "years_experience_max": 7, "role_function": "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.6dc9cbd3b241cef05f", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9a944f5cd5adec5c1d0d1067adbf4b0e", "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 Performance Engineer New York, NY | Hybrid | Remote About the role Serving frontier models at scale requires solving novel systems problems at every layer of the stack. As an Inference Performance Engineer, you'll own the runtime that turns accelerators into a production serving system, optimizing throughput, latency, and cost across thousands of nodes. You'll work alongside hardware and compiler teams operating at the frontier of AI silicon design. What you'll do - Build and improve the inference runtime - Design scheduling, continuous batching, KV cache, and prefill/decode disaggregation - Implement low-precision kernels and speculative decoding - Drive throughput, latency, and cost per token - Collaborate with hardware teams on kernels, operators, and graph optimizations - Own the OpenAI-compatible API surface and serving protocol - Build benchmarking, profiling, and regression infrastructure What you'll need - BS in CS, EE, or related field, or equivalent experience - Software engineering experience: Rust, Go, Python, or C++ - Understanding of concurrency, memory, and tail latency - Understanding of modern inference: transformers, attention, KV cache, batching, speculative decoding, quantization - Experience with model serving frameworks: vLLM, TGI, SGLang, TensorRT-LLM, llama.cpp, or custom runtimes - GPU or ASIC programming experience: CUDA, ROCm, Triton, or vendor-native toolchains - Experience with low-precision inference (FP8, FP4, INT4) - Profiling and benchmarking experience: Nsight, perf, custom harnesses What we offer - Top-tier compensation structured to recognize and retain the best talent - Meaningful equity - Comprehensive medical, dental, vision, life, and disability insurance - Parental leave for all", "matched_input": "kernel", "derivation_source": "candidate_result.raw_match_evidence", "candidate_evidence_id": "ev:gq_66eb6347ba344c23e0fd:9a944f5cd5adec5c1d0d1067adbf4b0e: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.c6ae15df16e50d7144", "query_id": "gq_66eb6347ba344c23e0fd", "job_id": "9a944f5cd5adec5c1d0d1067adbf4b0e", "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 Performance Engineer New York, NY | Hybrid | Remote About the role Serving frontier models at scale requires solving novel systems problems at every layer of the stack. As an Inference Performance Engineer, you'll own the runtime that turns accelerators into a production serving system, optimizing throughput, latency, and cost across thousands of nodes. You'll work alongside hardware and compiler teams operating at the frontier of AI silicon design. What you'll do - Build and improve the inference runtime - Design scheduling, continuous batching, KV cache, and prefill/decode disaggregation - Implement low-precision kernels and speculative decoding - Drive throughput, latency, and cost per token - Collaborate with hardware teams on kernels, operators, and graph optimizations - Own the OpenAI-compatible API surface and serving protocol - Build benchmarking, profiling, and regression infrastructure What you'll need - BS in CS, EE, or related field, or equivalent experience - Software engineering experience: Rust, Go, Python, or C++ - Understanding of concurrency, memory, and tail latency - Understanding of modern inference: transformers, attention, KV cache, batching, speculative decoding, quantization - Experience with model serving frameworks: vLLM, TGI, SGLang, TensorRT-LLM, llama.cpp, or custom runtimes - GPU or ASIC programming experience: CUDA, ROCm, Triton, or vendor-native toolchains - Experience with low-precision inference (FP8, FP4, INT4) - Profiling and benchmarking experience: Nsight, perf, custom harnesses What we offer - Top-tier compensation structured to recognize and retain the best talent - Meaningful equity - Comprehensive medical, dental, vision, life, and disability insurance - Parental leave for all", "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": { "remote_status": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/material/fb4b5605-9a2a-4e74-b4a9-80a03ee921db", "source_values": [ "rule:remote_status" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "equity_included": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/material/fb4b5605-9a2a-4e74-b4a9-80a03ee921db", "source_values": [ "derived:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" }, "seniority": { "tier": "posting", "verified": true, "label": "From the posting", "source_label": "the posting", "href": "https://jobs.ashbyhq.com/material/fb4b5605-9a2a-4e74-b4a9-80a03ee921db", "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/material/fb4b5605-9a2a-4e74-b4a9-80a03ee921db", "source_values": [ "llm:greenfield:inv_371_postfix" ], "checked_at": "2026-06-20T05:42:05.724Z" } } } ], "total": 494, "page": 13, "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": "9c86e540-8609-40e7-8263-3b66a70657c9", "issued_at": "2026-08-21T17:33:32.302Z", "route": "/jobs", "page": 13, "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": "7ca2c45245a5ffe0e10dd72529c36760", "position": 289, "page_position": 1, "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": 78, "quality_score": 35 } }, { "result_id": "160451bb4574c3b5bed32d9db241da1e", "position": 290, "page_position": 2, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3458164724220937, "rrf_score": 0.003968253968253968, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 92, "quality_score": 55 } }, { "result_id": "5533e2e54fd1a1cfde14904cd34cc3df", "position": 291, "page_position": 3, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.003968253968253968, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 256, "quality_score": 45 } }, { "result_id": "5ecb6623f9deb68ef21766b89181d865", "position": 292, "page_position": 4, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.003952569169960474, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 76, "quality_score": 55 } }, { "result_id": "9a449e6bca7a0540b313195298334e14", "position": 293, "page_position": 5, "retrieval_features": { "bm25_score": 0, "vector_score": 0.345890441781851, "rrf_score": 0.003952569169960474, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 101, "quality_score": 35 } }, { "result_id": "ade96efd59812c4f6635ec36243cadd7", "position": 294, "page_position": 6, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34599218690441813, "rrf_score": 0.003937007874015748, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 95, "quality_score": 35 } }, { "result_id": "615b3370b6e84af430fd64a7b7525b19", "position": 295, "page_position": 7, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.003937007874015748, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 242, "quality_score": 45 } }, { "result_id": "6cd82b05c0e9d8f1c9f6bfd8e8ed4b86", "position": 296, "page_position": 8, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.00392156862745098, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 175, "quality_score": 55 } }, { "result_id": "ab97cb1a70d9e0818de928afbf7168f2", "position": 297, "page_position": 9, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3461962731959646, "rrf_score": 0.00392156862745098, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 94, "quality_score": 35 } }, { "result_id": "475e08c7a8f517c1b785823c1a60773c", "position": 298, "page_position": 10, "retrieval_features": { "bm25_score": 0, "vector_score": 0.346324521741328, "rrf_score": 0.00390625, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 70, "quality_score": 35 } }, { "result_id": "80f444b8539c3a1d48a1be31f0ceff76", "position": 299, "page_position": 11, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.00390625, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 317, "quality_score": 45 } }, { "result_id": "8647a3558c189d2e467e2671dc0e8f47", "position": 300, "page_position": 12, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.0038910505836575876, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 105, "quality_score": 55 } }, { "result_id": "8e83ceb8837c8be0ca5a3c78a9b8af43", "position": 301, "page_position": 13, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3466042669651338, "rrf_score": 0.0038910505836575876, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 78, "quality_score": 35 } }, { "result_id": "93723d22d8c161ff74092de889333669", "position": 302, "page_position": 14, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.003875968992248062, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 211, "quality_score": 55 } }, { "result_id": "6eeb31d2f6397a871e074e099d3245f8", "position": 303, "page_position": 15, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3467336479742946, "rrf_score": 0.003875968992248062, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 70, "quality_score": 35 } }, { "result_id": "776097b1bbb887e627ec6725edca7e50", "position": 304, "page_position": 16, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3468736008741352, "rrf_score": 0.003861003861003861, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 101, "quality_score": 35 } }, { "result_id": "b68f3623b889efc81132a5b72306de62", "position": 305, "page_position": 17, "retrieval_features": { "bm25_score": 0, "vector_score": 0.34689174014975077, "rrf_score": 0.0038461538461538464, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 112, "quality_score": 45 } }, { "result_id": "9603805ed1c42d62bc3d87e645a12071", "position": 306, "page_position": 18, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.0038461538461538464, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": true, "posted_age_days": 86, "quality_score": 55 } }, { "result_id": "96f17cab1d11f8c7fffa556afe7f504a", "position": 307, "page_position": 19, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.0038314176245210726, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 304, "quality_score": 55 } }, { "result_id": "fdfd4b3f1d80adf8e5154bfd02187e4f", "position": 308, "page_position": 20, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3469315353713589, "rrf_score": 0.0038314176245210726, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 101, "quality_score": 35 } }, { "result_id": "9a49efc6fbe13b3cd7b1d28d3e0fc3ce", "position": 309, "page_position": 21, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.003816793893129771, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": true, "salary_disclosed": true, "posted_age_days": 226, "quality_score": 55 } }, { "result_id": "914cc80c6bfea4597e1af0b34197e9cd", "position": 310, "page_position": 22, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3469331057707593, "rrf_score": 0.003816793893129771, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 70, "quality_score": 35 } }, { "result_id": "a1e0509a9883eec6761ef30cf9a44a6f", "position": 311, "page_position": 23, "retrieval_features": { "bm25_score": 0, "vector_score": 0.3469354899564082, "rrf_score": 0.0038022813688212928, "text_match_title": false, "text_match_employer": false, "role_match": true, "benefit_verified": true, "salary_disclosed": false, "posted_age_days": 78, "quality_score": 35 } }, { "result_id": "9a944f5cd5adec5c1d0d1067adbf4b0e", "position": 312, "page_position": 24, "retrieval_features": { "bm25_score": -7.185502529144287, "vector_score": 0, "rrf_score": 0.0038022813688212928, "text_match_title": false, "text_match_employer": false, "role_match": false, "benefit_verified": false, "salary_disclosed": false, "posted_age_days": 72, "quality_score": 45 } } ] }, "context_signature": "OjQYe0g8vapkCiRNMwJr00-1HmONEu64YmxtZSPT6QA", "event_token": "OjQYe0g8vapkCiRNMwJr00-1HmONEu64YmxtZSPT6QA" } }