Jobs / United States / Nvidia Corporation
Product Engineer, Developer Platform
Nvidia Corporation · 🇺🇸 2 Locations
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 roles31 certified filings for “Engineer Senior SWQA Test Developer” (Software Quality Assurance Analysts and Testers) in CA: $164k–$201k, median $201k. 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
For more than 25 years, NVIDIA has driven innovation in computer graphics, PC gaming, and accelerated computing. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re harnessing the boundless possibilities of AI to build 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, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. DSX OS is NVIDIA's open source foundation for AI infrastructure. It is a growing portfolio of engineering teams building in the open. Every project in that portfolio needs to meet a shared bar for open-source readiness: code hygiene, governance, community health. Today, that's measured by hand, project by project. This role owns an internal product that changes that to a platform that scores, tracks, and reports on readiness across the DSX OS portfolio, and the roadmap that takes it from an internal utility to the definition of what "open source ready" means for DSX OS. What you'll be doing: • Lead the platform as production infrastructure. Maintain, harden, and evolve it from a measurement tool into the system every DSX OS project runs on — reliability and data quality matter because everything else in the program depends on it. • Automate scoring. Replace manual review-fix-recheck loops with tooling, so the portfolio scales through automation instead of added effort. Self-serve integration. • Build scoring into onboarding and workshop experiences. This lets participants get real-time feedback on their own project instead of waiting for manual review. It increases how many teams a single event can support. • Engineering team feedback loop. Work directly with the project teams using the platform, capture friction signals, and feed them back into the product instead of letting issues pile up in a backlog. • Readiness tracking and reporting. Automate the generation and tracking of per-project readiness plans so status, blockers, and progress are transparent to leadership without manual assembly. What we need to see: • 8+ years of relevant experience and a bachelors degree (or equivalent experience) • Experience leading a software product or internal platform end-to-end and making the calls on what gets built, why, and in what order • Strong software engineering skills, with a track record of turning manual, error-prone processes into reliable automation • Hands-on experience with agentic coding tools (e.g., Claude Code, Copilot, Cursor) as part of your actual engineering workflow, not just casual use • Experience running a production system in the real world — on-call, incident response, or otherwise owning uptime and reliability for something people depend on daily • Experience building reporting or dashboards that a non-engineering audience (leadership, partners) can trust as a source of truth • Ownership mentality for production systems — system dependability and integrity of data fall under your responsibility, not someone else 's Ways to stand out from the crowd: • Built or operated a rubric-based scoring or compliance system — quality gates, readiness checks, policy-as-code — where the output has to be defensible enough for people to act on it • Experience using LLMs within an evaluation pipeline (LLM-as-judge, qualitative signal assessment at scale) rather than only deterministic pass/fail checks • Designed a metric or signal taxonomy from scratch — deciding what to measure, how to weight it, and how to keep a single sc