Jobs / United States / Nvidia Corporation
Senior AI Engineer, AI tools
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 roles701 certified filings for “Engineer Senior Systems Software” (Software Developers) in CA: $173k–$214k, median $190k. Most were filed at wage level IV (82%) — 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
Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world. What you’ll be doing: • Evaluate emerging and established AI tools, agent harnesses, and models on representative NVIDIA engineering workflows, comparing task success, output quality, reliability, latency, cost, and enterprise readiness. • Spot opportunities where AI is the best tool. Uncover gaps, and recommend AI-first approaches over conventional solutions grounded in hands-on evaluation of modern AI-native tools. • Design, implement, and validate reference workflows for coding, testing, code review, and troubleshooting using tools such as Codex, Claude, Cursor, Greptile, and CodeRabbit. • Introduce technologies enabling massively parallel systems to improve turnaround time by an order of magnitude. • Optimize for performance and cost by identifying bottlenecks across training, evaluation, and testing workflows and improve throughput, latency, and efficiency • Collaborate with AI product vendors to gain deep insights of the AI industry, and share them with leaders and developers internally. • Work with core and emerging tool vendors to evaluate new capabilities, influence product roadmaps, resolve adoption issues, and introduce useful features into NVIDIA's AI ecosystem. • Provide AI tool and model selection guidance, recommended configurations, workflow examples, onboarding materials, and hands-on training. What we need to see: • MS in EE/CS or equivalent experience. 12+ years of work experience. • Strong understanding of large language models (LLMs), machine learning, and agentic AI, including how models, agent harnesses, context, and tool use affect end-to-end task outcomes. • Hands-on experience evaluating AI tools or models and applying LLMs to software engineering workflows, with skills in experiment design, benchmark development, reproducibility, and failure analysis. • Strong software engineering skills in Python, with experience in Java or Go and extensive scripting and automation experience. • Experience building evaluation pipelines, developer tooling, or full-stack applications, including API integration, data management, and deployment in cloud or enterprise environments. • Experience with tools for CI/CD setup such as Jenkins, Gitlab CI, Packer, Terraform, Artifactory, Ansible, Chef or similar tools. • Good understanding of distributed systems, understanding of microservice architecture and REST APIs. • Familiarity with software development, testing, code review, and build workflows, and the ability to translate evaluation results into clear technical recommendations and practical user guidance. • Ability to effectively work across organizational boundaries to enhance alignment and productivity between teams. Ways to stand out from the crowd: • Industry thought leader in AI, influenced AI ecosystem to deliver forward looking solutions • Expertise in agent harnesses, tool calling, MCP, retrieval-augmented generation (RAG), context management, or fine-tuning, and in evaluating their impact on complex engineering workflows. • Experience rolling out AI tools across engineering teams, collaborating with vendors, and turning emerging capabilities into measurable adoption and productivity improvements. • Experience developing large-scale distributed tooling, evaluation services, or observability systems