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# Senior Software Engineer, AI Compute Scheduling

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $184,000 - $287,500
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, C++ (Programming Language), Computer Programming, Computer Engineering, Software Debugging, Linux, InfiniBand, Python (Programming Language), Networking Basics, Open Source Technology, Graphics Processing Unit (GPU), High Performance Computing, Kubernetes, Information Technology, Slurm, Docker
- **Published:** November 11, 2026
- **Apply:** https://nvidia.wd5.myworkdayjobs.com/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Software-Engineer--AI-Compute-Scheduling_JR2029701

## About the Role

* BS or MS in Computer Science, Computer Engineering, Electrical Engineering or a related field, or equivalent experience.
 * 6+ years building production systems software.
 * Deep experience with at least one batch or cluster scheduler, such as Slurm, Kueue, Volcano, KAI, YuniKorn or a scheduler you built. You should be able to explain its internals, not only its configuration.
 * Strong programming in Go, C or C++, plus Python for tooling and analysis.
 * Hands-on Kubernetes experience: controllers or operators, CRDs, scheduling and resource management.
 * Solid Linux systems knowledge: containers (Docker, Podman, Enroot or Apptainer), cgroups, networking basics and debugging under load.
 * A track record of owning a service in production and making it measurably better.
 * Clear written and spoken communication with engineers and researchers.

Ways to stand out from the crowd:

 * Experience scheduling large-scale AI training: thousands of GPUs, NVLink and InfiniBand or RoCE topology, multi-node jobs that must start together.
 * Contributions to Slurm, Kubernetes SIG Scheduling, Kueue, Volcano, KAI or similar open-source schedulers.
 * Experience running Slurm on Kubernetes (Slinky or similar), or building Kubernetes operators for batch or HPC workloads.
 * Familiarity with RL, agentic or inference workloads and what they need from a scheduler: heterogeneous jobs, sandboxes, elastic scale and fast restarts.
 * Experience with workflow orchestrators such as Flyte, Airflow, Ray or Kubeflow at scale.