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# Senior Software Engineer, Distributed Systems Engineer - DGX Cloud

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Computing, Computer Clusters, Data Structures, Distributed Systems, Python (Programming Language), Software Engineering, System Programming, Event Driven Architecture, Kubernetes, Information Technology, Slurm
- **Published:** July 30, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=8914ca96f3ecc615

## About the Role

* Direct experience in a software engineering role within a highly technical organization with demonstrable impact from your work.
 * Highly motivated with strong communication skills, you can work successfully with multi-functional teams, principles, and architects and coordinate effectively across organizational boundaries and geographies.
 * 5+ years in similar role and experience on large-scale production systems. Experience with common software engineering principles, tools and techniques.
 * You possess a BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree or equivalent experience.
 * Technical knowledge, including a systems programming language (Go, Python) and a solid understanding of data structures and algorithms.

Ways to stand out from the crowd:

 * Technical competency in 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).
 * Prior experience in asynchronous workflows and/or event driven architecture.
 * Proven operational excellence in maintaining reliable and performant infrastructure.

## Description

* 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.
 * Designing and developing a massively distributed scalable platform which would be used to identify, diagnose and remediate non-performant GPU assets.
 * Working with teams across NVIDIA to ensure production AI clusters run reliability and consistently with maximum performance. Evaluating system failures and improving services based on a well-defined incident management process.


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