NVIDIAPosted 1d ago

Senior Software Engineer, DGX Cloud Production Engineering

Senior Software Engineer, DGX Cloud Production Engineering at NVIDIA scores 80 out of 100 on AI centrality, which makes it a Level 4 role on this board.

US, CA, Santa ClaraseniorFull time$184k-$288k

AI in this role

Senior Software Engineer needed to build and operate large-scale GPU infrastructure and automation for AI research and production workloads.

kubernetespythongoterraformargocd

ai-researchinfrastructureautomationgitopsdistributed-systems

NVIDIA DGX Cloud is building and operating large-scale GPU infrastructure for AI research and production workloads. We are looking for Senior Software Engineers to help build the automation, tooling, and operational systems that make GPU clusters reliable, scalable, and safe to run. This role is part of a production engineering team focused on Kubernetes-based infrastructure, GPU cluster operations, reliability, automation, GitOps, and Day 2 operability across DGX Cloud environments.

What you’ll be doing:

Build and operate automation for large-scale GPU clusters across NVIDIA Cloud Partners (NCP) and on-prem environments.
Develop tools and services for provisioning, validation, upgrades, monitoring, repair, and cluster lifecycle operations.
Improve Day 0 / Day 1 / Day 2 workflows for cluster bringup, handoff, and production operations.
Reduce manual production touches through APIs, GitOps, automation, and agent-assisted workflows.
Participate in on-call, incident response, debugging, and durable follow-up work.
Partner with platform, storage, networking, security, and workload teams to make infrastructure production-ready.

What we need to see:

8+ years of experience building or operating production infrastructure.
Strong programming skills in Python, Go, or similar.
Experience with Linux, Kubernetes, containers, cloud infrastructure, or infrastructure automation.
Ability to troubleshoot distributed systems in production.
Clear communication and ability to work across teams.
BS/MS in Computer Science or equivalent experience.

Ways to stand out from the crowd:

Experience with GPU infrastructure, Kubernetes operators, GitOps, Terraform, ArgoCD, or fleet automation.
Experience with SLOs, on-call, incident response, observability, and reliability practices.
Exposure to BMaaS, VMaaS, managed Kubernetes, or multi-cloud infrastructure.

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. We have some of the most forward-thinking and hard-working people on the planet working for us. If you're creative, hard-working and self-motivated, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 27, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI ResearchInfrastructureAutomationGitopsDistributed SystemsKubernetesPythonGo

Questions you could be asked

Tell me about a research question you investigated. What did you find?
Tell me about a project where infrastructure was part of your work. What did you do?
Tell me about a project where automation was part of your work. What did you do?
Tell me about a project where gitops was part of your work. What did you do?
Tell me about a project where distributed systems was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI Research, Infrastructure, Automation, Gitops, and Distributed Systems. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

Want your resume actually rewritten for this job?

The free preview above is everything we have today. A full resume rewrite is not live yet and has no price set. Join the waitlist and we will email you if we open it.