Physical Intelligence
ML Infra Engineer, Platform
San Francisco · On-site · Full-time · Posted Oct 9, 2026
- Experience
- 4-6 years working in fast-moving or early-stage environments.
About the job
Build and operate scalable, secure infrastructure and platform services supporting AI model training, simulation, and deployment to physical robots.
What you'll do
- Operate and evolve Kubernetes clusters and internal microservice platforms, including safe rollout, upgrade, and rollback strategies.
- Develop infrastructure foundations for security, observability, reliability, networking, and cost-aware operations.
- Improve developer experience through standardized, documented, self-service infrastructure and consistent local and remote workflows.
What they ask for
- Deep experience with cloud platforms (GCP, AWS) and distributed systems.
- Comfort with Kubernetes, cluster reliability, and service-oriented architectures.
- Experience with infrastructure-as-code (e.g. Terraform), containerization, and modern platform engineering practices.
- Familiarity with logging, metrics, tracing, incident response, SLOs, and debugging distributed systems.
Nice to have
- Experience with large-scale ML training, evaluation, or simulation infrastructure.
- Experience with secrets management systems (e.g., Doppler), observability, cost optimization, internal platform tooling, robotics, simulation, or real-time systems.
Tools
Kubernetes, GCP, AWS, Terraform, Doppler
AI focus
Build infrastructure supporting AI model training, evaluation, simulation, and deployment to physical robots; platform services also use agent-use as the primary interface.
Model-written summary · the original posting is authoritative