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NVIDIA

Designs the chips and software that power AI

Principal Software Engineer - DGX Cloud Production Engineering

Platform EngineeringConfirmed live in the last 24 hours
Full-timeRemote

About the Job

Requirements

  • A BS or MS degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
  • 15+ years of relevant software engineering experience, including experience building and operating large-scale production systems.
  • Deep expertise in Kubernetes internals, APIs, controllers or operators, and cluster lifecycle management.
  • A strong background in distributed systems design, reliability, scalability, and failure recovery.
  • Proven experience building platform software, infrastructure control planes, or foundations for managed services.
  • Strong programming skills in one or more systems or cloud-native languages, such as Go, Python, Rust, or C++.
  • Experience designing clear APIs and abstractions for platform consumers and engineering teams.
  • Demonstrated ability to provide technical leadership across team boundaries and drive ambiguous, cross-functional initiatives to completion.
  • A sustained record of significant technical contributions and recognized expertise influencing department-level architecture and high-priority company initiatives.

Responsibilities

  • Lead the architecture and development of core Kubernetes platform capabilities, including cluster management, control plane services, fleet lifecycle, and day-2 operations.
  • Design and build highly reliable distributed systems and APIs for provisioning, managing, upgrading, and remediating Kubernetes clusters at scale.
  • Define technical requirements, validation criteria, production-readiness practices, and the direction for declarative workflows and automation across the Kubernetes stack.
  • Collaborate across engineering teams to create cohesive platform experiences spanning management APIs, lifecycle orchestration, runtime integration, and fleet consistency.
  • Lead the diagnosis and resolution of complex platform issues spanning infrastructure, runtime, networking, hardware, and operations, improving the scalability, resilience, and operability of systems supporting large-scale AI deployments.
  • Influence engineering standards, architectural decisions, and long-term platform strategy.
  • Mentor senior engineers and raise the bar for design quality, execution, and engineering rigor across the organization.

Desired Qualifications

  • Experience building Kubernetes platforms or managed Kubernetes services.
  • Expertise in fleet management, cluster upgrades, node lifecycle, remediation, or day-2 operations.
  • Experience with declarative infrastructure, Kubernetes controllers, GitOps, or policy-driven platform automation.
  • Familiarity with both public-cloud and bare-metal infrastructure environments.
  • Experience supporting AI, GPU, high-performance computing, or other large-scale accelerated computing platforms.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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