Senior Infrastructure Engineer - GPU Compute

Boundless is coordinating GPU compute at scale as it becomes a leader in AI. As a Senior Infrastructure Engineer (GPU Compute), you'll build and operate the compute fabric that powers our AI inference workloads — a large, heterogeneous, globally distributed GPU fleet spanning consumer cards (including RTX 5090) and datacenter hardware. Your job is to keep that fleet full, fast, cheap, and always on: orchestrating workloads across regions and providers, squeezing every bit of performance out of the hardware, and driving down cost per GPU-hour. This role rewards engineers who want to go deep on bare-metal and GPU optimization. You should be comfortable operating with a high degree of autonomy, navigating ambiguity, and defaulting to a strong bias for action. What You'll Do GPU Fleet Orchestration: Operate a heterogeneous, multi-region GPU fleet (consumer + datacenter, including RTX 5090) using tools like SkyPilot, Kubernetes/k3s, and cloud + on-prem providers. Build the patterns that let us schedule inference workloads across the entire fleet reliably. Compute Scheduling & Utilization: Maximize GPU utilization across inference workloads. Own workload placement across spot, on-prem, and cloud capacity, keeping the "always-on inference substrate" saturated and economical. Bare-Metal & GPU Optimization : Go deep on GPU performance — PCIe P2P, ReBAR, NUMA topology (e.g. EPYC SP5), CUDA/driver tuning, memory configuration, and network topology — to push throughput per node. Reliability, Access & Observability : Build secure fleet access (Tailscale, Teleport), robust observability and alerting, and zero-downtime rollouts across a distributed node fleet. Cost Optimization: Drive down $/GPU-hr through spot instance management, intelligent workload placement between on-prem and cloud, and resource scheduling — without sacrificing reliability.