2 days ago
Base Salary
$184k - $357k/yr
Responsibilities
- Design and build scheduling for large GPU fleets using Slurm and Kubernetes, including queueing, gang scheduling, topology-aware placement, preemption, reservations, and backfill.
- Build policy systems for fair sharing, quotas, and allocations across research teams and schedulers.
- Run Slurm as a cloud-native Kubernetes service and extend Kubernetes batch scheduling through Kueue and related systems.
- Build reservation automation, node-health-aware placement, cross-cluster workflow orchestration, and APIs for submitting and tracking work.
- Measure and improve GPU occupancy, waste, fairness, and job start times.
- Own production reliability through on-call, incident analysis, and systemic failure remediation.
- Contribute to Slurm, Kubernetes, and related open-source projects while collaborating with platform, storage, networking, and research teams.
Requirements
- BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
- At least 6 years of experience building production systems software.
- Deep understanding of at least one batch or cluster scheduler, such as Slurm, Kueue, Volcano, KAI, YuniKorn, or an equivalent scheduler.
- Strong programming ability in Go, C, or C++, plus Python for tooling and analysis.
- Hands-on Kubernetes experience with controllers or operators, CRDs, scheduling, and resource management.
- Solid Linux systems knowledge, including containers, cgroups, networking fundamentals, and debugging under load.
- Track record of owning a production service and measurably improving it.
- Clear written and spoken communication with engineers and researchers.
- Preferred experience includes scheduling large-scale AI training across thousands of GPUs, NVLink and InfiniBand or RoCE topology, contributions to open-source schedulers, Slurm on Kubernetes, Kubernetes operators for batch or HPC workloads, and workflow orchestrators such as Flyte, Airflow, Ray, or Kubeflow.
Benefits
- Eligible for equity and benefits.
- Applications will be accepted at least until November 14, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.