A profitable, high growth technology company in the Bay Area is expanding the team that powers the compute behind its most important research and products. As a senior engineer, you will write production code that decides how large fleets of accelerators are scheduled, served and repaired. You will become the engineer researchers call when a critical run has to go right, with real ownership and visible impact from day one.

What you will do:

• Build and operate GPU clusters across public cloud, specialty GPU providers and owned data centers
• Design schedulers and automation that increase utilization and recover from hardware failures without manual intervention
• Stand up fast model serving and distributed training environments
• Report on where compute spend goes and where it can be reduced

What you bring:

• 5+ years engineering production distributed systems or ML infrastructure
• Strong Python plus Go or C++
• Deep Kubernetes experience, with exposure to Slurm or similar schedulers
• Proven experience running large GPU workloads on a modern training or serving stack
• Willingness to carry the pager for systems that other teams depend on