Jobs / United States / General Motors Company
Technical Lead Manager, Sensing Systems Design & Validation
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 roles1 certified filing for “Manager - Systems Engineering” (Information Technology Project Managers) in TX: $202k–$202k, median $202k. 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 Work Arrangement: This role is categorized as Remote/ hybrid . Remote: This role is based remotely but if you live within a 50-mile radius of [Austin, Detroit, Seattle, Warren, Milford, Sunnyvale ], you are expected to report to that location three times per week, at minimum. About the Organization General Motors is developing advanced driver assistance and autonomous driving systems designed to make personal transportation safer, more accessible, and more capable. The Autonomous Vehicle Architecture and System Design team defines the technical foundations that allow sensing, perception, machine learning, simulation, and vehicle software to work together reliably across real-world conditions. We are looking for an experienced Technical Lead Manager to lead the architecture and development of sensing-system evaluation capabilities. You will define how camera, LiDAR, and RADAR systems are characterized, simulated, integrated, and validated across the operational design domain. You will combine statistical analysis, empirical evidence, first-principles engineering, and software development to guide sensor architecture decisions and accelerate the delivery of safe, scalable autonomy systems. This role offers broad technical influence. You will work across sensing, perception, machine learning, simulation, vehicle integration, data, safety, and program teams. You will also create the tools, workflows, and technical alignment needed to turn large-scale experiments into clear engineering decisions. What you’ll do • Set the technical direction for sensing-system evaluation frameworks that measure performance, robustness, scalability, and readiness across representative driving conditions. • Lead integration of camera, LiDAR, RADAR, and related sensor models into simulation and evaluation environments. Define fidelity, performance, and usability targets for large-scale experimentation. • Build statistical and empirical methods that complement physics-based analysis. Use experiment design, confidence measures, failure analysis, and trend analysis to characterize sensor and perception performance. • Generate and manage synthetic data at scale from road-data replay, simulation, and scenario-generation pipelines. Partner with sensing and perception engineers to translate requirements into useful datasets. • Design data augmentation and scenario coverage for challenging conditions such as nighttime, rain, snow, fog, sensor contamination, sensor cleaning, occlusion, glare, and other sources of performance degradation. • Work with machine learning and perception teams to make synthetic and recorded datasets suitable for model training, evaluation, regression testing, and failure analysis. Lead initial model retraining or pipeline demonstrations when needed to reduce end-to-end technical risk. • Integrate new sensor components and candidate sensing architectures into the autonomous driving software stack. Identify interface, configuration, performance, and maintainability risks across software releases. • Define and improve developer tools for running sensing experiments, including application programming interfaces, command-line tools, notebooks, automated workflows, and dashboards that communicate results to technical and program leaders. • Develop evaluation metrics and reporting that connect sensor performance to system-level requirements, safety objectives, perception outcomes, and architecture trade-offs. • Lead technical reviews and cross-domain initiatives from ambiguous requirements through design, implementation, validation, and adoption. Build alignment across teams with different objectives, constraints, and levels of technical detail. • Identify opportunities to improve software archite