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About the Role

The Infrastructure team builds and operates the backbone of Physical Intelligence, supporting AI model training, large-scale simulation, and reliable deployment of intelligence across robot fleets. This role involves owning and scaling AI-native infrastructure, driving security and cost-awareness, hardening platform foundations, and improving developer experience.

Responsibilities

Own and scale AI-native infrastructure, including Kubernetes clusters and microservice platforms.
Operate and evolve Kubernetes clusters and service deployment patterns.
Build a scalable microservice platform for internal systems.
Support safe rollouts, upgrades, and rollback strategies.
Drive security, observability, and cost-aware infrastructure.
Build systems that surface reliability and performance issues early.
Improve cost visibility and enable cost-aware decision-making.
Harden platform foundations with multi-cloud considerations.
Design authentication/authorization flows, networking architecture, and quota/rate-limiting services.
Reduce infra churn by standardizing patterns, abstractions, and interfaces.
Improve developer experience by building clear, documented interfaces for platform infrastructure.
Support consistent local vs. remote development workflows.
Improve self-serve infrastructure usage.
Collaborate with researchers and engineers to understand requirements.
Translate fast-moving needs into reusable infrastructure.
Own systems end-to-end, from design through operation.

Requirements

Extremely strong first-principles thinking.
Familiarity with agent infrastructure, observability, security and sandboxing.
Deep experience with cloud platforms (GCP, AWS) and distributed systems.
Ability to reason about system bottlenecks, performance tuning, and cost optimizations.
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 with demonstrated growth trajectory.
Ability to spin up quickly on unfamiliar and ambiguous domains.
Strong sense of ownership.
Deep care about the company mission.

Skills

Kubernetes
Cloud Platforms (GCP, AWS)
Distributed Systems
Compute Orchestration
Networking
Autoscaling
Service Meshes
Load Balancing
Infrastructure-as-Code (Terraform)
Containerization
Logging
Metrics
Tracing
Incident Response
SLOs
Debugging Complex Distributed Systems
Agent Infrastructure
Observability
Security
Sandboxing
ML Training Infrastructure
ML Evaluation Infrastructure
ML Simulation Infrastructure
Secrets Management Systems (Doppler)
Robotics
Simulation
Real-time Systems

Experience Level

4-6 years

About the Company

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.

Equal Opportunity

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