Applied Researcher

Physical Intelligence is bringing general‑purpose AI into the physical world. We are a group 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 Deployments team is responsible for solving real world problems with our models and robots. We tackle the full problem space: integrating with customer workflows, training models to solve their dexterous tasks, and ensuring the on‑site reliability of the system. This breadth of problem space is why we’re a full‑stack robotics team – whether it’s thinking about customer‑facing experiences or fine‑tuning models for tasks no robot has done before, we put forth the best solution Pi has to offer.

• Deploy and debug learned policies on physical robots, diagnosing failures across the full stack (perception, policy, control, hardware).
• Train and tune policies, curate data, and iterate to improve real‑world performance.
• Write production‑quality code that interfaces with Pi’s infrastructure.
• Work with operators to set up tests, evals, and data collection pipelines.
• Engage with partners to understand use cases and observe robots in deployment contexts.
• Bridge research and operations: translate research advances into deployable systems, and surface real‑world failure modes back to researchers and (software and hardware!) engineers.
• Define and shape a vision for what real‑world deployments will look like in the long‑term.

• Hands‑on experience deploying robots or autonomous systems in real‑world environments.
• Strong engineering skills: clean Python, ability to interface with infrastructure, debugging instincts.
• Ability to debug the full stack from perception to control.
• Practical mindset: motivated by making things work, not by open‑ended research.
• Clear communication with researchers, operators, and occasionally partners.

• Founded or worked at an early‑stage robotics or AV company.
• PhD in relevant field.
• Intuition for policy training, neural network debugging, and data curation.
• Experience with robot manipulation platforms.

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