Senior Software Engineer, DGX Cloud Production & Automation

NVIDIA; California (MO); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Comprehensive benefits

Job summary

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.

Qualifications

• 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 across teams.

Responsibilities

• Build and operate automation for large-scale GPU clusters across NVIDIA Cloud Partners 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.

Skills

Python

Go

Distributed systems

Communication

Education

BS/MS in Computer Science or equivalent

Tools

Linux

Kubernetes

Terraform

ArgoCD

Job description

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.