Jobs / United States / General Motors Company

General Motors Company

Safety Data Analysis Engineer – AV Operational Safety and Issue Response (GPSSC)

Remote - United States · Remote · US only Posted today

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Where you can work from

United States only

Based on the listing's location: Remote - United States.

The company's headquarters is not the same as where you may work from. Visa sponsorship only matters if you would relocate; see below.

Visa sponsorship, if you would relocate

Sponsorship verdict

Sponsorship possible

One solid signal, not two. Worth applying, and worth asking about sponsorship early.

  • Employer is on a government sponsor recordThe US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
  • What General Motors Company paid sponsored hires in similar roles2 certified filings for “Senior ML Engineer - Embodied AI Offboard Percepti” (Data Scientists) in CA: $230k–$240k, median $235k.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
The US Department of Labor certified 606 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
What General Motors Company paid sponsored hires in similar roles
2 certified filings for “Senior ML Engineer - Embodied AI Offboard Percepti” (Data Scientists) in CA: $230k–$240k, median $235k. Most were filed at wage level III (100%) — 3 lottery entries, ≈46% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.

About the role

Job Description At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features.   Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. The Safety Assurance for Effective Autonomous Driving Software (SAFE-ADS) department is part of GM’s Global Product Safety, System, and Certification organization. Our mission is to help GM deliver trustworthy automated-driving products.  As the central authority for automated-driving system safety, the department brings together experts from across the company to develop and maintain a comprehensive safety case, including safety performance indicators for GM’s automated-driving technologies. GM’s vision is zero crashes, zero emissions, and zero congestion, and autonomous vehicle safety is essential to achieving that vision.  The Role The AV Safety Engineering Analytics team is seeking an experienced Safety Data Analysis Engineer to provide engineering support for AV Operational Safety and Issue Response. This is a safety engineering role focused on data-driven issue response and investigation. You will combine engineering judgment, safety analysis, and strong data skills to investigate potential issues, organize event-related data, and develop evidence that supports AV Safety Assurance and AV Operational Safety decisions.  The role is designed for an engineer who is highly proficient in navigating, integrating, designing, and analyzing large-scale data. You will support rapid, highly iterative investigations involving specific vehicles, locations, software releases, event sequences, operational conditions, and other issue-relevant dimensions.  The scale of AV operations, including potential issues, actual events, readiness drills, and ongoing software releases, creates a need for rapid, detailed, event-driven analyses. At the same time, you will help convert repeatable investigation patterns into reusable analytics and issue-detection methods.  What You'll Do • Respond collaboratively to AV Safety Strategy and Assurance and AV Operations requests for potential issue-specific analyses, event-related data organization, and root-cause investigations.  • Collaborate with Engineering, Operations, and Safety teams to investigate potential AV issues and conduct root-cause analyses.  • Translate investigation needs into clear analysis requirements, data requirements, analytical designs, and execution plans in collaboration with requesting teams.  • Establish and support rapid incident-response and targeted-analysis capabilities that leverage standing data pipelines as well as unique issue- and incident-relevant data sources.  • Integrate vehicle telemetry, time-series signals, event and incident records, readiness-drill data, software-release information, location and operational context, and other relevant sources into analysis-ready datasets.  • Distinguish data-quality, sensor, instrumentation, and pipeline issues from meaningful operational outliers using engineering judgment and data validation methods.  • Deliver concise, technically rigorous analysis findings, readouts, and evidence that help partner teams assess potential operations safety issues and determine appropriate next steps.  • Identify investigation workflows that can be generalized into reusable metrics, detection methods, pipelines, or analysis tooling without losing the flexibility

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