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Physical Intelligence is developing foundation models and learning algorithms to power robots and physically-actuated devices. As a Fullstack Software Engineer, you will build internal products that enable the company's research and operations teams to move faster.
You will own annotation tooling—building platforms and workflows for annotation generation, from user interfaces to backend pipelines. You'll make annotation legible by creating systems that track quality, cost, throughput, and coverage so researchers can understand what they're getting and make informed decisions.
You'll also own researcher request and planning workflows, redesigning how researchers and prototypers turn ambiguous research needs into executable work. You'll build research and ops-facing tooling including dataset browsing, eval dashboards, and throughput/quality/progress tracking for Production Ops across lab, warehouse, and real-world sites.
This role requires you to act as your own PM: gather requirements, prioritize work, define success metrics, write specs, ship tools, drive adoption, and iterate based on feedback. You'll ship production-quality software including reliable frontend interfaces, backend APIs, data models, dashboards, and cloud services.
You should have strong full-stack engineering experience building production web applications and APIs, particularly with React, TypeScript, and Python. Experience with relational databases (Postgres), analytical systems (ClickHouse), and queueing systems is expected. Comfort with cloud and containerized environments (GCP, Kubernetes) is required. You should be able to design workflows where people, models, and software function as one system, with strong product judgment and attention to detail. Experience working directly with users, iterating from feedback, and navigating ambiguous, evolving requirements is essential. You should be able to start with a practical v0 and build toward scalable, production-quality systems.
Bonus experience includes founding or early-employee background, building data labeling platforms or human-in-the-loop tools, building internal tools for research or robotics, experience with tasking systems or workflow orchestration, or familiarity with the specific stack mentioned.
