Jobs / United States / General Motors Company
Staff Systems Engineer - Autonomy Behavior
General Motors Company · 🇺🇸 Sunnyvale, California, United States of America
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. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- 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. 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 roles8 certified filings for “Software Systems Engineer” (Mechanical Engineers) in CA: $192k–$220k, median $209k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
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.
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Sponsor Radar — General Motors Company
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. USCIS also records 267 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for General Motors Company →
About the role
Job Description Role As a Staff Autonomy Behavior Systems Engineer, you will set the technical direction for how autonomous vehicle behavior is specified, evaluated, and supported by evidence. You will work across behavior, software, simulation, safety, legal, product, and operations to build a validation system that supports sound safety and launch decisions and can run continuously. You will bring deep experience evaluating autonomous vehicles in commercial fleets and understand the difference between a test that produces a result and a validation system people can trust. You will connect requirements, scene definitions, metrics, acceptance criteria, coverage, simulation validity, human references, and the decision process around the result. What You’ll Do • Set the systems engineering approach for behavior validation from product requirements through release and monitoring, including behavior decomposition, metrics, acceptance criteria, coverage, confidence, and traceability. • Lead validation strategy for major capabilities and cross-cutting behaviors by connecting safety claims, legal and compliance expectations, hazard analysis, requirements, simulation tests, and release evidence. • Set the direction for behavior evaluation and simulation at scale across synthetic tests, road data, and counterfactual references, including scenario sourcing, sampling, orchestration, data quality, and reproducibility. • Lead cross-functional technical reviews and guide validation tools, data pipelines, simulation workflows, and analysis products toward repeatable systems; turning complex results into clear recommendations. Required Qualifications • Master's degree in systems engineering, mechanical engineering, aerospace engineering, electrical engineering, computer science, robotics, or a related field. Advanced degree is a plus. • 8 or more years of professional experience in systems engineering, autonomous vehicles, robotics, vehicle development, simulation, or safety-critical validation. • A proven record of evaluating autonomous vehicles for a commercial fleet, launch, supervised release, or other high-consequence product decision. • Deep systems engineering fundamentals, including architecture and decomposition, use-case and scene modeling, requirements strategy, interface definition, verification and validation planning, and traceability. • Professional experience defining or leading simulation evaluation at scale, including the trade-offs between coverage, confidence, runtime, cost, and simulation validity. • Strong Python experience. You should be comfortable reading and writing production-quality analysis or validation software, reviewing designs and pull requests, and using code to test an engineering idea quickly. • Strong communication skills and the ability to create alignment among teams with different goals, terminology, and technical perspectives. • Demonstrated stakeholder management and technical leadership across organizational boundaries. You have led work through influence, not only through formal authority. • Evidence of setting direction beyond a single project, such as establishing a methodology, framework, lifecycle, platform, standard, or reusable validation process adopted by other engineers or teams. What Will Give You a Competitive Edge • Human benchmarking for behavior and safety evaluation, including attentive human benchmark construction, human percentile methods, expert review, and the limits of human references. • Counterfactual analysis in simulation, including AV versus human or AV versus alternate-policy comparisons under matched scene conditions. • Experience building or using a safety case, writing or reviewing safety claims, and determining what validation evidence is needed to suppor