Jobs / United States / Nvidia Corporation
Technical Lead, GenAI - Autonomous Vehicles
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 roles3 certified filings for “Technical Program Manager” (Information Technology Project Managers) in WA: $168k–$192k, median $184k. Most were filed at wage level IV (100%) — 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 — 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
We are seeking a highly technical and strategic Senior Technical Lead to join our team, with a focus on engaging developer ecosystems across emerging technology domains such as Generative AI, Autonomous Vehicles, and Simulation Platforms. In this pivotal role, you will work directly with software solution providers, developers, and industry professionals to foster the adoption of NVIDIA’s advanced AI and computing platforms—including Omniverse, Cosmos, and GenAI frameworks. The ideal candidate brings a blend of deep technical expertise and commercial go-to-market experience, combined with a passion for developer advocacy and a talent for communicating how NVIDIA technology can solve complex, real-world challenges. What You'll Be Doing: • Serve as a technical advisor and problem solver with partner engineering teams, collaborating on architecture, code, and integration for Omniverse and AI enabled-solutions. • Develop and maintain deep technical expertise in NVIDIA Cosmos and Omniverse Platforms and related technologies (APIs, USD, NIMs, Blueprints) through prototyping, technical integration and creation of reference architectures. • Advise on technical enablement resources such as sample code, guides, demonstration pipelines, and tools to highlight the application of technologies in solving real-world problems. • Engage with partner software organizations, from engineering teams to technical leaders, and decision-makers to understand their goals, solve technical challenges, and promote best practices for successful integrations. • Represent and advocate for the partner technical needs and feedback to NVIDIA’s internal product and engineering teams, supplying actionable insights from field deployments to influence product roadmaps. • Support product launches, technical go-to-market activities by providing technical validation, demonstrating integrated solutions, and ensuring excellence in customer- and partner-facing materials. • Guide partners and startups through onboarding and integration with NVIDIA’s programs, fostering co-innovation and the development of next-generation solutions. What We Need to See: • Master’s or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience. • 12+ years of experience of hands-on experience in a technical AI role, with a strong emphasis on AV End-to-End models and GenAI model development. • Experience writing production code in Python or C++, and proficiency with Linux. • Hands-on experience with DevOps tools such as GitLab, Docker, and Kubernetes. • Strong understanding of AV systems (Sensors, dynamics, perception, prediction, planning, control). • Experience with DL and RL algorithms and frameworks such as PyTorch. • Skilled at collaborating across engineering, product, sales, and marketing teams, with strong interpersonal abilities to simplify complex technical concepts for diverse audiences. • Experience leading technical collaborations with engineering and product teams—including architectural design, code reviews, technical mentorship, and delivery of technical talks or workshops. • Self-starter with a vision for growth, real passion for continuous learning and sharing findings across the team. Ways to Stand Out from the Crowd: • Experience with AV sensors, data curation pipelines, world models, simulations workflows and tools. • Experience with Agentic AI frameworks, tools, and protocols like LangChain, LangGraph, MCP or equivalent experience. • Understand computational characteristics of Multimodal LLMs, VLMs, DiT, etc. • Experience in deploying LLM models at scale on mainstream cloud providers (e.g., AWS, Azure, GCP) with a proven track record to profile and optimize inference latency and throughput, memory and I/O utilization. • Successful hi