Jobs / United States / Nvidia Corporation
Research Intern, World Models and Synthetic Data for Autonomous Driving - Summer 2027
Nvidia Corporation · 🇺🇸 US, CA, Santa Clara
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 2,374 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 394 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 Nvidia Corporation paid sponsored hires in similar roles83 certified filings for “Research Scientist” (Computer and Information Research Scientists) in CA: $164k–$201k, median $169k. Most were filed at wage level II (45%) — 2 lottery entries, ≈31% 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.
Or apply yourself on the official page →
Sponsor Radar — Nvidia Corporation
The US Department of Labor certified 2,374 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 394 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 Nvidia Corporation →
About the role
Be part of the Autonomous Vehicle (AV) Applied Research team and help advance safe autonomy through generative world models and synthetic data. This internship is an opportunity to work alongside a team of researchers and engineers developing the next generation of models that can understand, simulate, and reason about complex driving environments. We are looking for a passionate and highly motivated Research Intern with a strong background in machine learning and an interest in autonomous driving, generative world models, multimodal foundation models, and synthetic data generation. Our applied research team works closely with researchers and engineers across NVIDIA Research and the AV organization to develop scalable methods for training, evaluating, and improving autonomous systems. Our research spans generative video and world models, controllable scenario generation, multimodal learning, end-to-end driving, 3D computer vision, and data-centric learning. We are particularly interested in using generative models to create diverse, safety-critical, and long-tail driving experiences that can help improve the robustness and generalization of autonomous systems. As a Research Intern, you will contribute to research on generative data engines for autonomous driving: developing and using world models to understand data coverage, generate targeted driving scenarios, and study how synthetic data can systematically improve AV models. You will have the opportunity to develop new research ideas, run large-scale experiments, collaborate with experienced researchers and engineers, and contribute to publications at top-tier conferences. What you'll be doing: • Research and prototype generative world models, traffic world models, and synthetic-data generation methods for autonomous driving. • Develop traffic world models that capture multi-agent interactions and realistic, reactive behaviors, enabling simulation and generation of complex driving scenarios. • Develop methods for controllable generation of diverse, interactive, and safety-critical driving scenarios. • Explore how generative video models, multimodal foundation models, traffic simulation, data curation, active learning, self-supervised learning, and simulation can improve training-data quality, diversity, and coverage. • Design and conduct experiments to understand how generated data affects AV model performance, robustness, and generalization. • Translate recent advances in generative modeling and machine learning into practical approaches for autonomous driving. • Collaborate closely with researchers and engineers across NVIDIA on research prototypes and experimental systems. • Contribute to high-impact research publications and open research efforts. What we need to see: • Currently pursuing a Ph.D. or M.S. in Computer Science, Electrical/Computer Engineering, Robotics, or a related field, with research experience in deep learning, computer vision, generative modeling, multimodal learning, robotics, or autonomous driving. • Strong foundations in machine learning and deep learning, with hands-on experience developing and evaluating neural-network models. • Experience in one or more of the following areas: generative models, video generation, multimodal learning, 3D computer vision, autonomous driving, or robotics. • Strong mathematical and analytical skills and an interest in designing rigorous experiments. • Strong Python programming skills and experience with modern deep learning frameworks. • Self-motivated, collaborative, and comfortable working in a research environment with open-ended problems. Ways to stand out from the crowd: • Publications or research projects at conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, or related venues. • Hands-o