Jobs / United States / General Motors Company
2027 Summer Intern, Data Scaling, 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 Data Scaling team builds the data, machine learning, and infrastructure foundations that enable Embodied AI models to improve with scale. We work across data collection, curation, mining, labeling, dataset quality, model inputs, distributed training, experiment workflows, and the systems that help researchers and engineers develop, evaluate, and deploy models more efficiently. Our work supports large-scale autonomous driving models and the broader Embodied AI flywheel. We combine machine learning, data engineering, distributed systems, and software engineering to make high-quality, product-aligned driving data available for perception, planning, trajectory generation, and other autonomy capabilities. About the Role As an Embodied AI Data Scaling Intern, you will work on a well-scoped project that improves the scale, quality, efficiency, or reliability of data and model development for autonomous driving. You will collaborate with researchers and engineers to build data pipelines, analyze large datasets, improve training workflows, or develop infrastructure that increases the number and quality of experiments the team can run. Potential focus areas include: • Data Curation and Quality: Build methods to select, filter, balance, and validate large driving datasets aligned with product and modeling needs. • Data Mining and Scenario Discovery: Identify valuable, rare, or challenging driving situations and develop tools to improve coverage of long-tail scenarios. • Dataset and Labeling Pipelines: Improve automated labeling, data transformation, feature generation, and dataset release workflows. • Distributed Training and Model Scaling: Optimize the systems, input pipelines, and compute workflows used to train large models on large datasets. • ML Experimentation and Flywheel Infrastructure: Build tools that help teams develop, train, evaluate, debug, and deploy models more quickly and reliably. What You’ll Do • Develop data pipelines and tooling for large-scale, multimodal autonomous driving datasets. • Analyze data quality, coverage, distribution, and performance impact using quantitative methods. • Prototype and evaluate approaches for data mining, curation, labeling, sampling, or scenario discovery. • Improve training throughput, data loading, experiment reproducibility, or resource utilization in distributed computing environments. • Collaborate with machine learning researchers, data engineers, infrastructure engineers, and autonomy teams. • Build visualizations, metrics, dashboards, and evaluation workflows to communicate data and model behavior. • Contribute to production-quality software through design reviews, code reviews, automated testing, continuous integration, and documentation. • Present technical findings and document experiments, results, and recommendations. Required Qualifications • Currently enrolled in or pursuing a Master’s or Ph.D. degree in Computer Science, Machine Learning, Data Science, Electrical Engineering, or a related technical field. • Demonstrated experience through coursework, research, academic projects, or professional work in machine learning, data engineering, distributed systems, or a related area. • Strong programming skills in Python. • Experience working with data processing, databases, machine learning pipelines, or large-scale datasets. • Strong analytical and problem-solving skills, with the ability to use quantitative analysis to guide decisions. • Ability to work collaboratively in a cross-functional, team-oriented environment. • Strong written, verbal, and pres