Jobs / United States / General Motors Company
Machine Learning Engineer, AI Inference Solutions (Early in Career)
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 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 General Motors is a global leader in advanced driver assistance , with Super Cruise hands-free technology in more than 500,000 equipped vehicles on the road and over 700 million hands-free miles driven— demonstrating that automation can be trusted, intuitive, and helpful while reaching everyday drivers at unprecedented scale. Within GM AV, the Model Deployment & Inference Solutions team deploys machine learning models from training frameworks (e.g., PyTorch ) onto autonomous-vehicle hardware; our two-fold mission is to build the ML deployment platform that makes model rollouts fast and predictable, and to optimize models so they meet the real-time latency and memory budgets required to run on-vehicle. Our work sits on the critical path for GM’s publicly committed launch of eyes-off (hands-free, eyes-free) autonomous driving in 2028 on the Cadillac Escalade IQ, and we’re hiring engineers to help deliver the next generation of safe, delightful personal autonomous-vehicle experiences. About the R o le As an early career Engineer on the Model Deployment & Inference Solutions team, you’ll contribute across both sides of our mission: building the ML deployment platform and optimizing models for on-vehicle inference. You’ll work with and learn from senior engineers on real production deployments, platform features, and model-optimization workflows that ship to GM’s Super Cruise fleet at large scale, with structured mentorship and a clear onboarding plan. You’ll also collaborate closely with our sister teams (kernels, compiler , reduced precision, and parity) on the end-to-end path that takes trained models from research frameworks to ultra-efficient, safety-critical inference on the car. This is an early-career / new graduate role designed for candidates who have recently or will be completing their degree by August 2026. What You’ll Do (Responsibilities) • Contribute production code across the ML deployment platform , model-optimization workflows , and inference benchmarking/profiling infrastructure. • Pair with senior engineers on deployment workflows , performance investigations , model-optimization experiments (e.g., quantization, pruning, distillation), and platform tooling . • Build, test, and maintain platform tools (e.g., validators, performance probes, parity and sensitivity analyzers, agentic specialists) with technical guidance and code review support. • Investigate and help root-cause production deployment or performance issues; learn and apply the diagnostic playbook for compiler , kernel , runtime, and parity bugs. • Collaborate with cross-functional teams across the AV organization ; including kernels, compiler, reduced-precision, parity, and model-development groups—to plan and execute model deployments to the AV stack, working under the guidance of senior engineers • Participate in code reviews, design discussions, and technical documentation to ensure reliability, correctness, and clear abstractions in a large-scale codebase. • Learn and follow secure coding, safety, and compliance practices required for on-vehicle autonomous driving software. Your Skills & Abilities ( Required Qualifications ) • Recently completed or completing a Bachelor’s or Master’s degree by Spring 2026 in Computer Science , ECE , or a rela ted t echnica l field . (Degree must be completed before your start date. ) • Strong computer science fundamentals (e.g., data structures, algorithms, operating systems, computer architecture) and solid coding skills in Python and/or C++ , demonstrated through coursework, internships, or substantial projects. • Hands-on experience in AI/ML (