Jobs / United States / General Motors Company
2027 Summer Intern, AI Research, Embodied AI
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 Work Arrangement: Hybrid: This internship is categorized as on-site. The selected intern is expected to report to the office 3 days a week. Location: Sunnyvale, CA About the Team The Embodied AI Research team advances artificial intelligence methods for autonomous vehicles and embodied systems. We explore how models can combine visual and sensor understanding, language and other modalities, reasoning, prediction, and action to address challenging problems in autonomous driving. Our work includes foundation models, vision-language and vision-language-action architectures, generative and world models, self-supervised learning, imitation learning, reinforcement learning, multimodal learning, and methods for learning from large-scale driving data. We work closely with engineering teams to translate research into reliable systems for real-world autonomy. About the Role As an Embodied AI Research Intern, you will conduct applied research on a well-scoped project at the intersection of machine learning, robotics, and autonomous driving. You will work with experienced researchers and engineers to develop hypotheses, design experiments, train and evaluate models, analyze results, and communicate findings. Potential focus areas include: • Foundation Models for Autonomy: Develop or adapt large-scale models that learn useful representations and capabilities from diverse driving data. • Vision-Language-Action Models: Explore architectures that connect multimodal perception and high-level reasoning with autonomous vehicle decisions and actions. • Generative and World Models: Use generative techniques to model complex driving environments, improve scenario understanding, or support planning and simulation. • Learning for Planning and Control: Apply imitation learning, reinforcement learning, or other learning methods to improve prediction, decision-making, and vehicle behavior. • Multimodal and Temporal Learning: Build methods that reason over camera, lidar, radar, map, language, and time-series information. What You’ll Do • Formulate research problems and develop prototypes for autonomous driving applications. • Design and run experiments, ablation studies, and quantitative evaluations. • Train and benchmark models using large-scale datasets and distributed compute infrastructure. • Analyze model behavior, failure cases, generalization, and performance tradeoffs. • Collaborate with perception, planning, robotics, controls, and systems engineering teams. • Contribute to technical discussions, research documentation, publications, patents, or open-source work where appropriate. • Present findings clearly to technical and cross-functional audiences. Required Qualifications • Currently pursuing or in the process of obtaining a Ph.D. in Machine Learning, Artificial Intelligence, Computer Science, Robotics, or a related technical field. • Strong understanding of modern machine learning and deep learning methods. • Proficiency in Python and experience with PyTorch, TensorFlow, JAX, or another machine learning framework. • Demonstrated AI/ML research experience through coursework, academic projects, publications, or comparable work. • Strong analytical and problem-solving skills, with experience designing experiments and interpreting results. • Ability to work collaboratively in a cross-functional, team-oriented environment. • Strong written, verbal, and presentation skills. • Availability to work full-time, 40 hours per week, during the internship period. Preferred Qualifications • Experience with transformers, large language models, vision-language models, vision-language-action models, diffusion models, or other generative architectures. • Experience with reinforcement learning, imitation learning,