Site Reliability Engineer - AI Infrastructure

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.

• 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.

• 7+ years of experience in SRE, Dev Ops, 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.

• IPO Equity
• 10% company bonus
• 401K 4% match

• $250,000 gross per year