Job at a glance
Keen to join a company that champions growth and development? Join a seed-stage AI infrastructure company building large-scale training and inference platforms that were previously accessible only to hyperscalers. The business began with a single managed GPU cluster that quickly reached capacity and has since expanded into a global platform spanning infrastructure, networking, and orchestration. Don’t miss out on this exciting opportunity and apply today!
Responsibilities: Design, deploy, and maintain large-scale GPU clusters (H100/H200/B200) for training and inference workloads. Build automation pipelines for provisioning, scaling, and monitoring compute resources across Slurm and Kubernetes environments. Develop observability, alerting, and auto-healing systems for high-availability GPU workloads. Collaborate with ML, networking, and platform teams to optimise resource scheduling, GPU utilisation, and data flow.
Implement infrastructure-as-code, CI/CD pipelines, and reliability standards across thousands of nodes. Diagnose performance bottlenecks and drive continuous improvements in reliability, latency, and throughput. Skills / Must Have: 7+ years of experience in SRE, DevOps, or Infrastructure Engineering roles supporting large-scale compute environments. Strong hands-on experience with Kubernetes and Slurm for cluster orchestration and workload management.
Deep knowledge of Linux systems, networking, and GPU infrastructure (NVIDIA H100/H200/B200 preferred). Proficiency in Python, Go, or Bash for automation, tooling, and performance tuning. Experience with observability stacks (Prometheus, Grafana, Loki) and incident response frameworks. Familiarity with high-performance computing (HPC) or AI/ML training infrastructure at scale. Background in reliability engineering, distributed systems, or hardware acceleration environments is a strong plus.
Benefits: IPO Equity 10% comapny bonus 401K 4% match Salary: $250,000 gross per year +