Are you looking for an exciting new opportunity? Join a seed-stage AI infrastructure company building large-scale training and inference platforms previously accessible only to hyperscalers. The business began with a single managed GPU cluster that reached capacity almost immediately and has since expanded into a global platform spanning infrastructure, networking, and orchestration. If you would like to learn more about this opportunity, feel free to reach out 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 optimize resource scheduling, GPU utilization, 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% company bonus • 401K 4% match Salary: • $250,000 gross per year