ML Infra Engineer, Platform

Physical Intelligence is bringing general-purpose AI into the physical world. We are a team of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.

The Infrastructure team builds and operates the backbone of everything PI does: from training state-of-the-art VLA models, to orchestrating large-scale simulation, to reliably deploying intelligence across fleets of physical robots. The team works closely with researchers, robotics runtime, product, and platform engineers to ensure infrastructure scales from prototype to production-grade deployments.

• Extremely strong first-principles thinking.

• Familiarity with agent infrastructure, observability, security and sandboxing

• Deep experience with cloud platforms (GCP, AWS) and distributed systems: compute orchestration, networking, autoscaling, service meshes, load balancing.

• Ability to reason about system bottlenecks, performance tuning, and cost optimizations across compute, networking, and storage.

• Comfort with Kubernetes, cluster-level reliability, and service-oriented architectures.

• Solid intuition around scalability, performance, and failure modes.

• Experience with infrastructure-as-code (e.g. Terraform), containerization, and modern platform engineering practices.

• Familiarity with logging, metrics, tracing, incident response, SLOs, and debugging complex distributed systems.

• Strong cross-functional communication and ownership mindset.

• Experience (4-6 years) working in fast-moving or early-stage environments where ambiguity is normal with demonstrated growth trajectory.

Ultimately, we’re looking for someone who can spin up quickly on unfamiliar and ambiguous domains, has a strong sense of ownership, and cares deeply about our mission. Even if you don’t check all the boxes above, we strongly encourage you to apply if this sounds like you.

• Experience with large-scale ML training, evaluation, or simulation infrastructure.

• Experience with secrets management systems (e.g., Doppler).

• Background in observability, cost optimization, or internal platform tooling.

• Exposure to robotics, simulation, or real-time systems.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.