Jobs / United States / Nvidia Corporation
Senior Production Engineer - DGX Cloud
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 roles2 certified filings for “Senior Network Operations Engineer - DGX Cloud” (Computer Network Architects) in VA: $163k–$163k, median $163k. Most were filed at wage level III (100%) — 3 lottery entries, ≈46% 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 DGX Cloud delivers AI services and endpoints for research and production workloads. We are looking for a Senior Production Engineer to build software and automation that make those services reliable, scalable, and safe to operate. The Production Engineering team works on large-scale distributed systems spanning internal and external model endpoints; regional control plane services that orchestrate workloads and route requests; and the GPU/CPU compute infrastructure where inference and agentic workloads run. Our work spans Kubernetes clusters across AWS, Azure, Google Cloud, other partner cloud environments, and on-premises deployments. What you’ll be doing: • Build and operate production software, automation, and tooling for control plane services, model deployments, and inference and agentic workloads across DGX Cloud environments. • Improve the reliability of inference and agentic platforms and services, including NVIDIA Cloud Functions, SGLang- and vLLM-based endpoints, and inference services built with NVIDIA Dynamo, through health validation, safer rollouts, observability, and recovery. • Improve endpoint availability, inference routing, capacity management, and service health to maintain predictable performance as workloads and demand change. • Use infrastructure as code and GitOps to deploy, configure, validate, upgrade, and recover services consistently across environments. • Build workflows for service enablement, model releases, handoff, deprecation, and ongoing operations; replace repeatable manual work with reliable automation. • Define and instrument SLIs and SLOs for inference and control plane services, including availability and latency, use error budgets to guide reliability improvements, and make production health visible to partner teams. • Participate in on-call and incident response, troubleshoot failures across routing, model runtimes, software, and infrastructure, and turn recurring issues into automation and durable fixes. • Collaborate with model, platform, storage, networking, security, and GPU infrastructure teams to design and operate services safely at scale. What we need to see: • 8+ years of experience building or operating production services and large-scale distributed systems, including hands-on automation. • Strong programming skills in Python, Go, or a comparable language, with experience developing tools for production operations. • Experience with infrastructure as code, configuration management, or GitOps, and with building automation for repeatable service deployments and changes. • Strong knowledge of Linux, Kubernetes, containers, cloud infrastructure, distributed systems, and networking fundamentals; ability to diagnose failures in production. • Understanding of SRE principles, including SLIs, SLOs, error budgets, incident response, and reducing operational toil. • Experience instrumenting services and using metrics, logs, and traces to understand system behavior and improve reliability. • Clear technical communication and ability to work across engineering teams. • BS/MS in Computer Science or equivalent experience. Ways to stand out from the crowd • Familiarity with technologies such as vLLM, SGLang, PyTorch, TensorRT-LLM, NVIDIA Dynamo, CUDA, or NCCL, and with GPU performance analysis. • Experience building Kubernetes operators, controllers, workload orchestration services, fleet management systems, or self-healing automation. • Experience with Terraform, Argo CD, CI/CD, policy validation, or safe deployment and rollback systems. • Experience developing with AI tools and agents. • Background with production AI inference or agentic workloads, including debugging issues across models, runtimes, Kubernetes, and hardware. NVIDIA is leading the way in groundb