ML Infra Engineer (Supercomputing)

Physical Intelligence builds general-purpose AI for the physical world. Training our models requires orchestrating thousands of accelerators across a heterogeneous fleet of GPU and TPU clusters — spanning different hardware generations, cloud providers, and cluster topologies. Today, researchers often need to know which cluster to target, what resources are available, and how to configure their jobs accordingly. That doesn't scale. We need a scheduling and compute layer that makes the right placement decision automatically — routing jobs to the best cluster based on availability, hardware fit, cost, and priority — so researchers can focus entirely on the science. This role owns that problem end-to-end: the scheduling systems, the placement logic, the cluster management layer, and the operational tooling that keeps it all running. This is not cloud DevOps. It's not about standing up clusters and walking away. It's a systems role for people who care about intelligent resource allocati