> Markdown version of [/jobs/ext/4721259-senior-software-engineer-ai-compute-scheduling](https://www.wearedevelopers.com/jobs/ext/4721259-senior-software-engineer-ai-compute-scheduling). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior Software Engineer, AI Compute Scheduling

- **Company:** NVIDIA Corporation
- **Location:** Durham, NC, United States
- **Experience:** Expert
- **Salary:** $184,000 - $287,500
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, C++ (Programming Language), Cloud Computing, Computer Programming, Computer Engineering, Computer Graphics, Software Debugging, Linux, InfiniBand, Python (Programming Language), Networking Basics, Open Source Technology, Software Engineering, Reinforcement Learning, Computer Gaming, Graphics Processing Unit (GPU), High Performance Computing, Kubernetes, Information Technology, Slurm, Docker
- **Published:** November 14, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-ai-compute-scheduling-durham-nc--9c34f043-12d5-4623-b212-e301084cbe4f

## About the Role

Software Engineering, Artificial Intelligence (AI), Computer Graphics, Artificial Intelligence (AI), GPU (Graphics Processing Unit), Computer Games, Topology, Reinforcement Learning, Vehicle Fleets, Cloud Computing, * 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.