We are looking for experienced Member of Technical Staff, High Performance Computing Engineers to help build and scale the infrastructure that trains our frontier models and powers the next evolution of our personal AI, Copilot.
\nThis role offers the unique opportunity to work on some of the largest scale supercomputers in the world – a rare chance to operate at such a significant scale.
\nResponsibilities
\nDesign, operate, and maintain large-scale HPC environments, drawing on hands-on engineering experience in production settings.
\nOwn the deployment, configuration, and day-to-day operation of HPC schedulers (e.g., SLURM, Kubernetes), ensuring reliable and efficient job scheduling at scale.
\nServe as a technical owner for at least one core HPC domain (GPU compute, high-performance storage, networking, or similar), including ongoing maintenance, performance tuning, and troubleshooting of massive clusters.
\nDevelop and maintain automation and tooling using Bash and/or Python to improve cluster reliability, observability, and operational efficiency.
\nPartner closely with researchers and engineers to support their workloads, troubleshoot cluster usage issues, and triage failed or underperforming jobs to resolution.
\nDrive work forward independently by navigating ambiguity and technical roadblocks, delivering incremental improvements that get capabilities into users’ hands quickly.
\nQualifications
\nDo you have a Bachelor’s degree in computer science, or related technical field AND 4+ years technical engineering experience with deploying or operating on-premise or cloud high-performance clusters, AND 4+ years experience working with high-scale training clusters (ex. working with frameworks/tools such as nvidia InfiniBand clusters, SLURM, Kubernetes, Ray, etc.), AND 4+ years experience building scalable services on top of public cloud infrastructure like Azure, AWS, or GCP, OR equivalent experience?
\nPreferred Qualifications
\nMaster’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with deploying or operating on-premise or cloud high-performance clusters, AND 6+ years experience working with high-scale training clusters (ex. working with frameworks/tools such as nvidia InfiniBand clusters, SLURM, Kubernetes, Ray, etc.), AND 6+ years experience building scalable services on top of public cloud infrastructure like Azure, AWS, or GCP, OR equivalent experience.
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