Jobs / United States / General Motors Company
Senior Software Systems Engineer - Autonomous Vehicles
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 The Role You will be part of a team that drives systematic and data-driven improvements to autonomous vehicle software by designing, implementing, and maintaining robust processes for evaluation and validation. We are looking for a highly motivated individual with excellent analytical skills to own end-to-end execution, improve evaluation methodologies, and communicate insights that establish confidence in the quality of our end-to-end ML stack. In this position, you will work closely with AI/ML engineers, simulation engineers, systems engineers, and data partners to identify, analyze, monitor, and prioritize the signals used to assess performance. You will leverage simulation and on-road data to build scalable processes for evaluating coverage, metrics, uncertainty, and validation confidence. If you are interested in having a major impact on accelerating validation confidence for ML-driven autonomy through creative problem solving, let’s chat! What you’ll be doing • Define AV evaluation and validation processes from initial concept through implementation, including test-framework requirements, scenario and test-suite design, coverage, metrics, and the evidence needed to assess confidence in system performance. • Design and implement scalable testing and simulation frameworks for test generation, execution, data collection, result aggregation, and reproducible analysis. • Provide hands-on implementation of infrastructure and data solutions to assess confidence in AV performance using simulation and on-road data. • Develop and apply methods to evaluate simulation validity, sim-to-real correlation, and the predictive value of simulation results. • Proactively scope and identify metrics, sampling approaches, and analytical methods needed to improve evaluation workflows and close gaps in evidence. • Contribute to automated triage and root-cause analysis strategies for AV deficiencies, regressions, and uncertainty in an end-to-end stack. • Articulate insights, summaries, limitations, and recommendations to engineers, technical leaders, and other stakeholders based on continuous analysis of AV performance. • Define and maintain scalable processes to identify, monitor, and improve evaluation KPIs and confidence measures. • Help connect continuous-improvement activities to the evidence needed to support safety, systems, and downstream readiness decisions. What you must have • Strong Python programming skills, with experience building clear, maintainable analysis, evaluation, or testing tools. • Experience designing or implementing testing, simulation, or evaluation frameworks for complex software/hardware or cyber-physical systems. • Experience with GitHub, Jira, or equivalent tools. • Demonstrated end-to-end ownership, from defining a problem through delivering and communicating the desired outcome. • A track record of analytical and systems-engineering work involving complex software/hardware systems or ambiguous AI functions. • Experience performing root-cause analysis and applying analytical methods to system or behavioral performance data. • Ability to creatively solve problems with limited supervision, learn quickly, and operate effectively in a fast-paced environment. • Strong cross-functional communication skills, including the ability to communicate data-driven findings to leadership. • Bachelor’s, master’s, or doctoral degree in engineering, physics, applied mathematics, statistics, data science, or a related discipline, or an equivalent combination of education and experience. Bonus points! • Experience with autonomous vehicles, ADAS, robotics, or production-grade robotic systems. • Hands-on experience with simulation environments, scenario generation, large-scale test execution, or analy