Drimva

NVIDIA

Principal Software Engineer, DGX Cloud Production Engineering

$272K–$431K

US, CA, Santa Clara +1 · Principal · Full-time · Posted Aug 11, 2026

Location
US, CA, Santa Clara
US, Remote
Experience
15+ years of relevant software engineering experience.
Education
BS or MS degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.

About the job

Leads the architecture and development of Kubernetes platform systems for provisioning, operating, upgrading, and scaling clusters across cloud and on-premises environments, including infrastructure supporting large-scale AI.

What you'll do

  • Lead development of Kubernetes cluster management, control plane services, fleet lifecycle, and day-2 operations.
  • Design reliable distributed systems and APIs, and set technical requirements and production-readiness practices for platform automation.
  • Resolve complex platform issues and guide technical strategy, engineering standards, and senior engineers.

What they ask for

  • Deep expertise in Kubernetes internals, APIs, controllers or operators, and cluster lifecycle management.
  • Strong background in distributed systems design, reliability, scalability, and failure recovery.
  • Experience building and operating large-scale production systems and platform software, infrastructure control planes, or managed-service foundations.
  • Strong programming skills in one or more of Go, Python, Rust, or C++; ability to lead cross-team initiatives and design platform APIs.

Nice to have

  • Experience with managed Kubernetes services, fleet management, cluster upgrades, node lifecycle, remediation, or day-2 operations.
  • Experience with declarative infrastructure, Kubernetes controllers, GitOps, or policy-driven automation; public-cloud and bare-metal environments; or AI, GPU, or HPC platforms.

Pay

272,000 USD - 431,250 USD base salary

Tools

Kubernetes, Go, Python, Rust, C++, GitOps

AI focus

Builds and operates Kubernetes platform capabilities supporting large-scale AI deployments and accelerated computing.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)