Support day-to-day operations of production on-premises and multi-cloud AI/HPC clusters, ensuring system health, user satisfaction, and efficient resource utilization.
Directly administer internal research clusters, conduct upgrades, incident response, and reliability improvements.
Develop and improve our ecosystem around GPU-accelerated computing including developing scalable automation solutions.
Maintain heterogeneous AI/ML clusters on-premises and in the cloud.
Support our researchers to run their workloads including performance analysis and optimizations
Analyze and optimize cluster efficiency, job fragmentation, and GPU waste to meet internal SLA targets.
Support root cause analysis and suggest corrective action.
Proactively find and fix issues before they occur.
Triage and support postmortems for reliability incidents affecting users or infrastructure.
Participate in a shared on-call rotation supported by strong automation, clear paths for responding to critical issues, and well-defined incident workflows.

Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience
Minimum 2+ years of experience administering multi-node compute infrastructure
Background in managing AI/HPC job schedulers like Slurm, K8s, PBS, RTDA, BCM (formerly known as Bright), or LSF
Proficient in administering Centos/RHEL and/or Ubuntu Linux distributions
Proven understanding of cluster configuration management tools (Ansible, Puppet, Salt, etc.), container technologies (Docker, Singularity, Podman, Shifter, Charliecloud), Python programming, and bash scripting.
Passion for continual learning and staying ahead of emerging technologies and effective approaches in the HPC and AI/ML infrastructure fields.

AI and ML HPC Cluster Engineer

Key skills