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.
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 May 31, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
Additional Information
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.
Nvidia · Santa Clara, CA