Jobs / United States / General Motors Company
Sr. Machine Learning Engineer
General Motors Company · 🇺🇸 San Francisco, 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 roles3 certified filings for “Senior Machine Learning Engineer” (Software Developers) in WA: $150k–$230k, median $225k. Most were filed at wage level IV (67%) — 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 GM is developing the next generation of embedded platform software to deliver state-of-the-art ADAS features to our customers. We are looking for an expert Sr. Camera ISP Engineer to join our Autonomous Vehicle team and help us build the best self-autonomous system in the world. In this position, you will be part of the Autonomy Interface Software team, which is responsible for developing camera data pipelines for autonomous vehicles. You will interact with camera hardware engineers, embedded software engineers, systems engineers, Embodied AI engineers, technical program managers internally, and various external imager component vendors to ensure high-quality cameras are designed, manufactured, and tuned. You will be working closely with an embedded software team to enable Camera ISP functionality on new hardware. If you are looking to solve one of today’s most complex engineering challenges, see the results of your work in hundreds of self-driving cars, and make a positive impact in the world, join us. What You’ll Do • Responsible for delivering Camera Image Signal Processing (ISP) tuning optimized for autonomous vehicle use-cases • Work closely with cross-functional teams and external vendors on specifications, system definition, software development, and image quality tuning and evaluation • Design and execute comprehensive objective and subjective image quality evaluations using lab charts and real-world scenes, selecting appropriate metrics and analyses for each, leveraging tools such as Imatest and other chart/scene-based analysis methods, and developing GM-specific test scripts and IQ specification analysis tools . • Solid understanding of image sensors to o ptimize HDR sensor configurations and ISP settings for autonomous driving scenarios, aligning objectives and success metrics across EAI, system, and sensor teams. • Work with sensor validation and vehicle fleet teams to perform ISP characterization, image quality tuning and evaluation, and troubleshoot image quality related issues • Collaborate with machine learning engineers and hardware teams to architect sensing strategy for autonomous vehicles at scale • Innovate new techniques and conduct studies in optimizing ISP and performance for machine perception Your Skills & Abilities (Required Qualifications) • BS, MS/ PhD in imaging science, EE, CS, or similar field with 4 + years of hands-on experience in camera ISP tuning and evaluation • Must have hands-on experience tuning various ISP modules – AE, AWB, HDR processing, noise reduction and sharpness, tone mapping, and color tuning • Experience with different ISP architectures and camera development • Experience characterizing image sensors and tuning image sensor settings • Experience with at least one image quality evaluation software, preferably Imatest • Proficiency in programming languages like C/C++ or Python • E xperience in image processing and data analysis What Will Give You a Competitive Edge (Preferred Qualifications) • Experience with automotive ISP tuning is a big plus • Exposure to firmware design and/or real-time systems • Experience with vendor management and manufacturing processes • Hands-on experience optimizing image sensor–level IQ settings and operating modes to balance noise, dynamic range, motion blur, and artifacts for ADAS/autonomous use cases. • Experience with optimizing camera ISP for computer vision algorithms or M achine Learning . • Experience evaluating how sensor hardware and optical design impact image quality and ISP tuning trade-offs. Compensation: The compensation information is a good fai