Jobs / United States / Nvidia Corporation
Senior Staff Software Engineer — Agentic AI Applications and Foundations
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
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC-gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU-accelerated deep learning ignited modern AI—the next era of computing—with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” Our Enterprise AI team builds intelligent AI agents that transform how NVIDIA operates — from smart personal assistants and engineering-productivity tools to data-driven analytics and supply-chain optimization. These agents are live, in production, and used across the company. Now we need a senior staff-level, hands-on engineer to make them bulletproof and to architect the next generation of agent infrastructure. This is not a research role. This is a role for someone who obsesses over reliability, polish, and user trust — and who has the full-stack depth to harden production systems and the architectural vision to ensure they scale. What you'll be doing: • Improve reliability, performance, observability, release confidence, and end-user experience across desktop, web, and service-based AI products. • Design and build resilient frontends, backend APIs, distributed services, data flows, and deployment systems that scale to enterprise use. • Establish strong patterns for testing, debugging, CI/CD, safe rollout, auto-update mechanisms, monitoring, incident response, and operational excellence so our Agentic AI applications behave like mature software, not prototypes. • Build reusable capabilities that support multiple agent domains, including orchestration services, deep-agent workflows, memory and context services, evaluation frameworks, telemetry, and policy-aware tool integration. • Help validate and operationalize technologies such as Nemotron, NVIDIA AI Blueprints, and related platform capabilities in enterprise production settings. • Codify architecture, shared components, documentation, and operational playbooks; mentor engineers; and create foundations that are durable, reusable, and broadly owned. • Define the core architecture for how AI agents discover one another, collaborate securely, build trust, and operate under enterprise governance. • Partner closely with domain AI engineers, product managers, designers, infrastructure teams, IT, and research to deliver measurable outcomes across employee productivity, engineering efficiency, AIOps, and enterprise operations. What we need to see: • BS, MS, or equivalent experience in Computer Science or a related field. • 12+ years building and operating production software systems, including significant experience leading architecture and delivery across the full stack. • Familiarity with enterprise application deployment, security, authentication, device management, and application lifecycle management. • Solid experience building modern applications across frontend, backend, and platform layers. This may include technologies such as TypeScript/JavaScript, React, Electron or similar desktop frameworks, Python, Go, Java, APIs, data systems, and distributed infrastructure. • Proven track record taking complex products from prototype to reliable, secure, well-operated production systems. Deep expertise in testing strategy, release engineering, observability, performance tuning, and incident response. • Experience building shared services, internal platforms, SDKs, or core infrastructure used by multiple teams or products. • Working knowledge of modern AI application patterns such as LLM-powered applications, RAG, tool use, CLI-based workflows, reusable skills, MCP-based integrations, evaluation loops, memory systems, and agentic workflows. You