Principal Software Engineer, GPU Compute

Serve as the GPU technical leader for the Compute team, partnering across Kubernetes, Machine Bootstrap, Networking, and Cloud to drive GPU strategy end to end.

Own the GPU host lifecycle above raw fleet management: driver, firmware, and CUDA stack management, GPU health and telemetry, and remediation of GPU-specific failures (XID errors, ECC, thermal, NVLink and fabric faults).

Architect how GPU capacity is exposed to compute platforms, including scheduling, isolation, and integration with Kubernetes for GPU and AI workloads.

Drive GPU reliability and performance at fleet scale, defining the detection, diagnosis, and automated repair of unhealthy accelerators before they impact production.

Evaluate and onboard new GPU and AI accelerator platforms, networking topologies (NVLink, InfiniBand, RoCE), and multi-node training and inference patterns.

Establish the standards, tooling, and APIs that let other engineering teams consume GPU compute safely and efficiently, reducing toil and raising the bar for the org.

10+ years of experience building and operating large-scale distributed systems and infrastructure.

Deep, hands-on GPU expertise at the machine management layer and above: GPU host provisioning, driver and firmware lifecycle, GPU health and reliability, and the realities of running accelerators in production.

A track record as an expert for compute, not just fleet management, with the scars to prove you have scaled GPU or accelerator infrastructure that other teams depend on.

Strong proficiency in Go or other well-structured programming languages.

Experience operating GPU and AI workloads in production, including familiarity with CUDA, GPU scheduling, and high-performance networking (NVLink, InfiniBand, RoCE).

Familiarity with Kubernetes for GPU workloads and with bare-metal concepts (firmware, BMC/IPMI/Redfish, OS imaging) is a strong plus.