Principal Software Engineer, GPU Compute

Every day tens of millions of people come to Roblox to explore create play learn and connect with friends in 3D immersive digital experiences all created by our global community of developers and creators.

At Roblox were building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together from anywhere in the world and on any device. Were on a mission to connect a billion people with optimism and civility and looking for amazing talent to help us get there.

A career at Roblox means youll be working to shape the future of human interaction solving unique technical challenges at scale and helping to create safer more civil shared experiences for everyone.

As a Principal Software Engineer on the Compute team you will be the technical anchor for Robloxs GPU and AI accelerator capabilities. This is a battle-tested GPU expert role focused on the machine management layer and above: how GPU hosts are made production-ready kept healthy and turned into reliable compute for the workloads that depend on them. You will own the hard problems that show up only at scale from driver and firmware management to GPU health reliability and performance across a rapidly growing fleet of accelerators spanning Roblox data centers and cloud environments. You will set the technical direction for GPU compute and up-level the entire organizations GPU expertise.

You will:

• 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.

You have:

• 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.