Senior Software Engineer, Distributed Systems Engineer - Dgx Cloud

NVIDIA NVIDIA · Semiconductors · NC +4 · Remote

NVIDIA is seeking a Senior Software Engineer to scale its AI Infrastructure, focusing on production systems for large GPU clusters used in AI workloads. The role involves designing and developing distributed platforms for identifying, diagnosing, and remediating non-performant GPU assets, ensuring reliable and performant AI cluster operations.

What you'd actually do

  1. You will be part of an DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads.
  2. Designing and developing a massively distributed scalable platform which would be used to identify, diagnose and remediate non-performant GPU assets.
  3. Working with teams across NVIDIA to ensure production AI clusters run reliability and consistently with maximum performance.
  4. Evaluating system failures and improving services based on a well-defined incident management process.

Skills

Required

  • significant software engineering experience
  • cluster operations
  • operator development
  • node health monitoring
  • GPU resource scheduling
  • systems programming language (Go, Python)
  • data structures and algorithms
  • BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree or equivalent experience

Nice to have

  • managing and automating large-scale distributed systems independent of cloud providers
  • Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, Slurm, Base Command Manager)
  • asynchronous workflows
  • event driven architecture
  • operational excellence in maintaining reliable and performant infrastructure

What the JD emphasized

  • production systems
  • large scalable GPU clusters
  • AI workloads
  • distributed scalable platform
  • non-performant GPU assets
  • production AI clusters
  • maximum performance
  • system failures
  • incident management process
  • large-scale production systems
  • common software engineering principles, tools and techniques
  • large-scale distributed systems
  • cluster management systems
  • reliable and performant infrastructure

Other signals

  • production systems
  • large scalable GPU clusters
  • AI workloads
  • distributed scalable platform
  • AI-based applications