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Physical Intelligence is building general-purpose AI for the physical world through foundation models and learning algorithms for robots and physically-actuated devices. The Infrastructure team operates the backbone of the company's operations: training state-of-the-art VLA models, orchestrating large-scale simulation, and reliably deploying intelligence across robot fleets.
In this role, you will own and scale AI-native infrastructure, including operating and evolving Kubernetes clusters and building a scalable microservice platform for internal systems such as evaluation services, operational tooling, and internal APIs with agent-use as the primary interface. You'll support safe rollouts, upgrades, and rollback strategies.
You will drive security, observability, and cost-aware infrastructure by treating logging, metrics, tracing, and alerting as first-class platform primitives. You'll build systems that surface reliability and performance issues early and improve cost visibility to enable cost-aware decision-making at the infrastructure level.
You will harden platform foundations with multi-cloud considerations, designing authentication/authorization flows, networking architecture, quota and rate-limiting services, and cloud primitives that behave predictably. A major focus is reducing infrastructure churn by standardizing patterns, abstractions, and interfaces.
You will improve developer experience by building clear, documented interfaces for platform infrastructure, reducing friction between "I need infra" and "I can run my workload." This includes supporting consistent local vs. remote development workflows and enabling self-serve infrastructure usage.
You will collaborate closely with researchers and other engineers to understand requirements, translate fast-moving needs into reusable infrastructure, and own systems end-to-end from design through operation.
REQUIREMENTS:
- 4-6 years of experience in fast-moving or early-stage environments with demonstrated growth trajectory
- Extremely strong first-principles thinking
- Deep experience with cloud platforms (GCP, AWS) and distributed systems: compute orchestration, networking, autoscaling, service meshes, load balancing
- Comfort with Kubernetes, cluster-level reliability, and service-oriented architectures
- Ability to reason about system bottlenecks, performance tuning, and cost optimizations across compute, networking, and storage
- 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
- Familiarity with agent infrastructure, observability, security, and sandboxing
- Strong cross-functional communication and ownership mindset
BONUS:
- 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
