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# Principal Software Engineer, GPU Compute

- **Company:** Roblox
- **Location:** San Mateo, CA, United States
- **Experience:** Expert
- **Salary:** $345,040.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Intelligent Platform Management Interface, Bootstrap (Software), Cloud Computing, Nvidia CUDA, Distributed Systems, Firmware, InfiniBand, Structured Programming, Kubernetes, Bare Metal
- **Published:** June 5, 2026
- **Apply:** https://careers.roblox.com/jobs/7978492?gh_jid=7978492

## About the Role

* 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.
 * A history of being the anchor expert that an organization relies on for its hardest GPU and compute problems, and the leadership to up-level the engineers around you.

## Description

* 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.


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