Senior GPU Supercomputer Scheduler Engineer

What you'll do

• Design and develop new scheduling features to improve GPU occupancy, resource fairness, and power usage.
• Build and develop batch workload management and orchestration services for large-scale GPU clusters.
• Perform performance analysis and optimizations for deep learning workflows.
• Develop large-scale automation solutions to manage and monitor GPU-accelerated computing ecosystems.
• Conduct root cause analysis and implement corrective actions for system problems at various scales.
• Provide technical support to staff and end users to resolve batch scheduler issues.
• Identify architectural directions for scheduling many simultaneous multi-node GPU workloads with complex dependencies.

What we're looking for

• Bachelor’s degree in Computer Science, Electrical Engineering, or related field or equivalent experience.
• 5+ years of work experience.
• Strong understanding of batch scheduling, preferably with experience in schedulers such as SLURM or K8s batch schedulers (preferred).
• Significant experience in systems programming languages such as C/C++ and Go, as well as scripting languages like Python and bash.
• Established experience in Linux operating system, environment, and tools.
• Experience analyzing and tuning performance for a variety of AI workloads.
• In-depth understanding of container technologies like Docker, Singularity, and Podman.
• Knowledge in High-performance computing, Open Source Software Contribution, and deep learning frameworks like PyTorch and TensorFlow (preferred).