Jobs / United States / General Motors Company
2027 Summer Intern – AI/ML Engineer, Autonomous Vehicles: Simulation (PhD)
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 roles2 certified filings for “Senior ML Engineer - Embodied AI Offboard Percepti” (Data Scientists) in CA: $230k–$240k, median $235k. Most were filed at wage level III (100%) — 3 lottery entries, ≈46% 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 About the role As a PhD AI/ML Engineering Intern within General Motors’ Autonomous Vehicles organization, you will advance reliable AI/ML capabilities by translating deep research into validated, production-oriented systems. Your work may support perception, prediction, planning, decision-making, embodied AI, simulation, sensor understanding, mapping, or large-scale model development. You will work with real-world driving and sensor data, modern machine learning methods, and autonomous vehicle evaluation environments. You will collaborate with researchers and engineers to design experiments, build and assess models, investigate failure cases, and integrate promising approaches into broader AV systems. What you’ll do • Research and prototype advanced machine learning methods for perception, prediction, planning, or decision-making. • Design, train, evaluate, and improve models using large-scale multimodal driving and sensor data. • Build data pipelines, experimentation workflows, and evaluation tools for rapid model iteration. • Validate models through simulation, testing, performance analysis, and failure-case investigation. • Optimize models and systems for scalability, latency, reliability, and deployment constraints. • Collaborate with engineering and research partners to integrate prototypes into autonomous vehicle systems. • Document findings, present technical insights, and contribute to publications or other research outputs. Required qualifications • Currently enrolled full-time in a PhD program in computer science, machine learning, artificial intelligence, robotics, engineering, or a related STEM field. • Must have at least one additional quarter/semester of school remaining following the completion of the internship • Demonstrated depth of AI/ML research through publications, research projects, advanced coursework, or equivalent technical work. • Strong understanding of modern machine learning and deep learning methods. • Proficiency in Python and hands-on experience with an ML framework such as PyTorch, TensorFlow, or JAX. • Experience designing experiments, analyzing results, and applying quantitative problem-solving methods. • Strong programming, debugging, and software development fundamentals. • Strong communication skills and the ability to collaborate across research and engineering disciplines. • Availability to work full-time, 40 hours per week, during the internship period. Preferred qualifications • Candidates are not expected to meet every qualification listed below. Depending on the needs of a specific Autonomous Vehicles team, a combination of these skills and areas of knowledge may be relevant. • Conduct research in autonomous vehicles, ADAS, robotics, computer vision, or embodied AI. • Demonstrate knowledge of foundation models, transformers, generative and diffusion models, or vision-language architectures. • Apply multimodal learning techniques to camera, lidar, radar, or other sensor data. • Use self-supervised, imitation, reinforcement, or deep reinforcement learning methods. • Train models on large-scale datasets using distributed, parallel, or high-performance computing environments. • Develop ML systems, data pipelines, experimentation frameworks, or production-oriented model infrastructure. • Validate models through simulation, closed-loop environments, or real-world driving scenarios. • Apply expertise in trajectory prediction, behavior planning, motion planning, perception, mapping, or structured scene understanding. • Program in C++ or other systems programming languages. • Optimize models and systems using GPU programming, CUDA, accelerator frameworks, or performance profiling. • Apply numerical optimization, statistical estimation, probabilistic