Senior Software Engineer, Distributed Systems Engineering - DGX Cloud
About The Position
NVIDIA is hiring an experienced software engineer to help scale AI infrastructure for DGX Cloud. The role contributes to production systems that enable large, scalable GPU clusters to support a variety of AI workloads, including custom software for scheduling GPU resources on Kubernetes. The engineer will develop monitoring and health-management capabilities to improve the reliability, availability, and scalability of GPU assets, using data streams that include GPU hardware diagnostics as well as cluster and network telemetry. The role also works across NVIDIA teams to keep production AI clusters reliable, consistent, and performant, evaluates system failures, and improves services through a defined incident-management process.
Requirements
- Direct experience in a software engineering role within a highly technical organization, with demonstrable impact.
- Software development experience with Kubernetes APIs and frameworks, beyond operating a cluster.
- At least 8 years of experience in a similar role and experience with large-scale production systems.
- Experience with common software engineering principles, tools, and techniques.
- Strong communication skills and the ability to work with multifunctional teams, principals, and architects across organizational boundaries and geographies.
- A BS in Computer Science, Engineering, Physics, Mathematics, or a comparable degree, or equivalent experience.
- Knowledge of a systems programming language such as Go or Python.
- Solid understanding of data structures and algorithms.
Nice To Haves
- Technical competency managing and automating large-scale distributed systems independently of cloud providers.
- Advanced hands-on experience and deep understanding of cluster management systems such as Kubernetes, Slurm, or Bright Cluster Manager.
- Proven operational excellence maintaining reliable and performant AI infrastructure.
Responsibilities
- Develop custom software for scheduling GPU resources on Kubernetes.
- Implement monitoring and health-management capabilities for GPU assets.
- Use GPU hardware diagnostics, cluster telemetry, and network telemetry to support system health and reliability.
- Work with teams across NVIDIA to ensure production AI clusters operate reliably and consistently with maximum performance.
- Evaluate system failures and improve services through a defined incident-management process.
Benefits
- Equity
- Benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
