Jobs / United States / General Motors Company
Senior Software Systems Engineer, Autonomous Systems Validation Confidence
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).
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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 About the role We are looking for a Senior Software Systems Engineer to develop the methods, software, and quantitative evidence used to measure confidence in autonomous vehicle validation results. This role sits at the intersection of software engineering, systems engineering, simulation, statistics, and data science. You will help answer questions such as: Do our tests provide sufficient coverage? Are our metrics meaningful? Can simulation results predict real-world performance? How confidently can we detect a regression or support a release decision? This role is a strong fit for someone who has experience with both programming and physical or cyber-physical systems such as autonomous vehicles, robotics, aerospace, industrial systems, or other complex engineered products. What you’ll do • Develop scalable frameworks and methods for measuring validation confidence across simulation and real-world testing, including coverage, sampling, metric quality, statistical significance, and regression detection. • Build tools and data pipelines for test execution, analysis, metric computation, scorecards, and confidence reporting. • Evaluate simulation validity and road predictive power using measurable, defensible criteria; analyze test and vehicle data to identify uncertainty, pipeline issues, regressions, and gaps in evidence. • Translate validation claims and release questions into requirements, experiments, test suites, metrics, and quantitative decision criteria. • Improve the throughput, repeatability, and quality of validation workflows. • Partner with simulation, safety, autonomy, and release teams, and communicate conclusions, assumptions, limitations, and recommendations clearly. Required qualifications • Strong programming skills in Python, C++, or a comparable language, with experience writing clear, testable, maintainable code. • Experience applying engineering or quantitative methods to a physical, cyber-physical, or other real-world system. • Ability to turn ambiguous validation questions into measurable requirements, metrics, experiments, or decision criteria, and investigate complex behavior using incomplete or noisy data. • Ability to collaborate across disciplines and explain technical results with appropriate precision and context. • Bachelor’s degree in engineering, physics, applied mathematics, statistics, data science, or a related technical field, or equivalent practical experience. Preferred qualifications • Experience with autonomous vehicles, robotics, simulation, aerospace, industrial automation, or another safety-relevant engineered system. • Experience with verification and validation, test automation, scenario generation, requirements-based testing, or performance benchmarking. • Experience designing coverage measures, scorecards, confidence metrics, or regression-detection methods. • Experience with simulation-to-real-world correlation, predictive-validity analysis, or comparing results across test environments. • Applied knowledge of probability, statistics, experimental design, or data analysis, including hypothesis testing, confidence intervals, power analysis, sampling strategies, or precision and recall. • Experience with data pipelines, SQL, scientific computing, or large-scale test execution. • Graduate degree or equivalent depth in engineering, physics, applied mathematics, statistics, data science, or a related field. What success looks like • Validation results have clear, quantitative confidence and known limitations. • Coverage, metrics, and sampling are tied to the claims and decisions they support. • Regressions and progressions are detected reliably, simulation performance is evaluated against real-world outcomes, and the evidence supports decisions ab