Own the full lifecycle of GPU compute clusters — procurement, provisioning, configuration management, monitoring, and deprecation — across heterogeneous Linux environments (DGX, HGX, embedded systems)
Design and scale storage solutions (NFS, Lustre, WekaFS, or equivalent) with a clear roadmap for capacity and performance growth
Lead automation of infrastructure using modern IaC tools (Ansible, Terraform) and CI/CD pipelines (GitLab)
Manage and optimize job scheduling via Slurm, including fair-share policies, reservation management, and MIG/GPU partitioning strategies
Maintain and improve observability stacks (Prometheus, Grafana, DCGM) and drive proactive resolution of hardware and software incidents
Collaborate with ML engineers and software teams to tune cluster configuration for large-scale distributed training workloads
Evaluate and introduce new technologies — networking fabrics (InfiniBand, NVLink, EFA/RDMA), storage tiers, container runtimes — to improve performance and reliability
Mentor junior engineers and contribute to team-wide engineering standards

BS/MS in CS, EE, CE, or equivalent hands-on experience
5+ years of experience deploying and administering large-scale HPC or ML training clusters
Deep expertise in Linux systems administration at scale
Strong scripting and automation skills in Python and/or bash
Hands-on experience with Slurm (scheduling, accounting, cgroup configuration)
Proficiency with configuration management and IaC (Ansible required; Terraform a plus)
Experience with container technologies (Docker, Apptainer/Singularity, Kubernetes)
Solid understanding of high-speed networking (InfiniBand, RoCE, RDMA, EFA)
Experience with distributed/parallel filesystems and storage architecture
Ability to own problems end-to-end and communicate clearly with engineering and management stakeholders

Senior HPC Cluster Administrator – Deep Learning Frameworks

Key skills