Senior Software Engineer, AI Compute Scheduling

NVIDIA

. Design and build scheduling for large GPU fleets on Slurm and Kubernetes, including queueing, gang and topology-aware placement, preemption, reservations and backfill .

Posted 11/10/2026full-timeUnited StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building scheduling systems for large GPU fleets using Slurm and Kubernetes, with a strong focus on performance optimization and reliability. Proficient in programming languages such as Go, C, C++, and Python, alongside hands-on experience with cloud-native services and workflow orchestration tools.

Highest-signal resume keywords

Slurm SchedulingKubernetes ExperienceProduction Systems Software DevelopmentGPU Scheduling for AI TrainingOpen-Source Contributions

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Hard Skills

Batch SchedulingCluster SchedulingProgramming in GoProgramming in CProgramming in C++Python Tooling and AnalysisLinux Systems KnowledgeWorkflow OrchestrationIncident AnalysisService Reliability

Soft Skills

Clear Communication

Tools & Technologies

KubernetesSlurmDockerPodmanEnrootApptainerFlyteAirflowRayKubeflow

Industry Keywords

GPU OccupancyFair Share PolicyResource ManagementAI TrainingMulti-Node Jobs

Tech Stack

Tools & technologies

AirflowCloudDockerKubernetesLinuxNode.jsPythonRayC++Go

About the role

Key responsibilities & impact

Design and build scheduling for large GPU fleets on Slurm and Kubernetes, including queueing, gang and topology-aware placement, preemption, reservations and backfill
Build the policy layer for fair share, quotas and allocations across research teams
Run Slurm as a cloud-native service on Kubernetes and extend Kubernetes batch scheduling through Kueue and its ecosystem
Build scheduler-related services for reservation automation, node-health-aware placement, cross-cluster workflow orchestration and work-submission APIs
Measure and improve GPU occupancy, waste, fairness and time to start for large jobs
Own reliability through on-call, incident analysis and fixes that prevent recurring failures
Contribute to Slurm, Kubernetes and related open-source projects
Partner with platform, storage, networking and research teams

Requirements

What you’ll need

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
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
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 scheduler requirements such as heterogeneous jobs, sandboxes, elastic scale and fast restarts
Experience with workflow orchestrators such as Flyte, Airflow, Ray or Kubeflow at scale

Benefits

Comp & perks

Equity
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