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# Software Engineer - DGX Cloud

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, United States (Remote available)
- **Experience:** Expert
- **Salary:** $224,000.0
- **Contract:** Permanent contract
- **Skills:** Automation of Tests, Data Structures, Data Synchronization, Data Center Infrastructure Management (CIM), Distributed Systems, Fault Tolerance, Python (Programming Language), Linux System Administration, Remote Direct Memory Access, Data Streaming, System Programming, Kubernetes, Information Technology, Slurm, Machine Learning Operations
- **Published:** October 28, 2026
- **Apply:** https://startup.jobs/senior-software-engineer-dgx-cloud-2100-nvidia-usa-10598754

## About the Role

* 12+ years of experience in a similar role working on large-scale production systems.
 * BS in Computer Science/Engineering/Physics/Mathematics or other comparable degree, or equivalent experience.
 * Expert level knowledge of a systems programming language (Go, Python, and/or Rust) and a solid understanding of Data Structures and Algorithms.
 * Strong background in Linux system administration and management.
 * Deep knowledge of container orchestration platforms such as Kubernetes and/or SLURM.
 * Understanding of performance, security, and reliability in complex distributed systems.
 * Familiarity with system level architecture, data synchronization, fault tolerance, and state management.

Ways to stand out from the crowd:

 * Proficiency in designing and building widespread distributed networks.
 * Experience managing large-scale distributed systems independently of cloud providers.
 * Extensive expertise in datacenter operations and GPU hardware.
 * Hands-on experience working with RDMA networking.
 * Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, SLURM).

## Description

* Contribute to a comprehensive platform that automates GPU asset provisioning, configuration, and lifecycle management across cloud providers.
 * Build end-to-end automation of data center management, break/fix, and the full span of system upkeep for large-scale Machine Learning systems.
 * Implement monitoring and health management capabilities that enable industry-leading reliability, availability, and scalability of GPU assets.
 * Harness multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry.
 * Build automated test infrastructure that we use to qualify distributed systems for operation.
 * Partner with engineering teams across NVIDIA to ensure the software integrates seamlessly from the hardware all the way up to the AI training applications.