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Physical Intelligence
San Francisco, California, United States
Source: Physical Intelligence careers · View original posting
From Physical Intelligence's posting. “We” and “our” refer to the employer.
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.
As a Fullstack Software Engineer, you will build internal products that help Pi's research and operations teams move faster. You will work closely with operators, roboticists, researchers, and other engineers to turn physical workflows into reliable software.
Fullstack builds the internal software the rest of Pi runs on. Our users span the path from research intent to physical execution: researchers designing experiments and requesting data, Research Ops working across task design, environments, prototyping, and instructions, and Production Ops running collection, evals, and deployments across our lab, warehouse, and real-world sites.
In This Role You Will
The software people use to run, monitor, and debug robots, from setup and calibration through live operation and review. Someone lives in this for an entire shift, and small friction compounds fast.
Time on a robot is a fraction of the day. The rest is preparation, troubleshooting, handoffs, and feedback, spread today across several tools and several people. The goal is not to expose every tool, but to build one surface that tells someone what deserves their attention now and notices when they're stuck.
Make state unambiguous, make failure obvious, and make the safe action the easy one. Assume the network drops mid-episode or a camera dies, and design so the system degrades predictably rather than surprisingly.
Video, robot state, and control at latency budgets tight enough that teleoperation feels direct, plus the controllers, headsets, pedals, and sensors people drive with.
Decide what has to stay local because it is safety-critical or latency-sensitive, and what belongs in a control plane that owns identity, state, and coordination across sites.
On-prem and on-device deployment, versioning and rollout across lab, warehouse, and partner sites, and enough observability to know what is running where.
Instrument these interfaces well enough that we can see where the operation loses time, then test changes against it.
Gather requirements, prioritize, define success metrics, ship, and drive adoption. That starts by standing in the room and watching a shift.
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