Jobs / United States / Nvidia Corporation

Nvidia Corporation

Senior Technical Program Manager - LLM Safety

US, CA, Santa Clara Posted today

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Skills this job asks for

LLMsMachine learningLeadership

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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.
  • 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.
  • 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.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
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. Source: LCA disclosure data (US Department of Labor (OFLC)).
No salary bar for this route
H-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 roles
3 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).

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.

About the role

At NVIDIA, we redefine what’s possible in digital imaging, personal computer gaming, and high-performance computing. Now, we lead the charge into AI’s unlimited potential. Our GPUs act as the brains of computers, robots, and self-driving cars. They enable these machines to understand and interact with the world in new ways. This is your chance to join a legacy of innovation and excellence. Work with the world’s best talent in a diverse and encouraging environment. Join us and make a lasting impact on the world! This is an ambitious opportunity to work at the forefront of AI safety and contribute to groundbreaking advancements in technology. What You’ll Be Doing: • Lead cross-functional planning and execution for evaluation identification, selection and execution. With a focus on agentic-safety evaluations, including multi-turn tool calling, unsafe tool use, unintended actions, excessive autonomy, and multi-step failures. • Track execution, results for all evals, analysis, and the mitigation and research plans for improvement that will stem from results analysis.  Translate evaluation findings into prioritized mitigation plans with accountable owners, committed dates, and measurable closure criteria • Lead end-to-end safety planning and execution for Nemotron models, including program scope, dependencies, risks, resources, and release-readiness criteria. • Translate technical safety priorities into executable programs covering LLM security, frontier risks, agentic safety, hallucinations • Establish and track multi-turn, multi-modal, multi-lingual, long-context, and reasoning model evaluations across relevant use cases, domains, languages, modalities, and model releases. • Establish governance for reviewing safety findings, assigning severity, determining release impact, and escalating unresolved risks. Ensure required safety evidence, approvals, exceptions, and risk-acceptance decisions are documented before release. Identify program gaps and critical dependencies early and drive decisions, escalations, recovery plans, and corrective actions. • Build (with AI for Code/AI for Work) and maintain dashboards and executive reporting for evaluation coverage, critical findings, mitigation progress, residual risk, and release readiness. What we need to see: • Bachelor’s degree or equivalent experience in computer science, engineering, data science, or a related technical field. • 10+ years of experience in technical program management, engineering, product development, technical operations, or a similar area. • Strong understanding of LLM architecture, frameworks (e.g., OpenAI, Anthropic, Hugging Face), and model evaluation. • Familiarity with LLM development, post-training, inference, tool calling, evaluation datasets, and model-release lifecycles. • Experience directing programs related to AI/ML development, model evaluation, agentic safety, security, content safety, hallucinations, and/or production releases. • Familiarity with AI safety risks such as hallucinations, timely injection attacks, unsafe tool use, data poisoning, model manipulation, and unintended agent behavior. • Experience supporting model-safety evaluations, Red Teaming, adversarial testing, security assessments, or responsible-AI programs. • Ability to interpret technical evaluation findings and communicate their product, schedule, and release implications to leadership. Ways to stand out from the crowd: • Experience with LLM Agentic Safety, Security, Red Teaming, Hallucinations, adversarial assessment, or Responsible AI. • Experience defining evaluation datasets, rubrics, benchmarks, and release gates. • Ability to use evaluation results and data to make clear release recommendations and communicate residual risk to leadership. • Experience craft

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