Production Test Engineer
Physical Intelligence · San Francisco
CHECKED 14H AGO
Roboticsonsitesenior
At a glance
Highlights
- Own the manufacturing software platform powering PI's robotic systems.
- Turn prototype scripts into safe, guided workflows for technicians.
- Build traceability for devices, test results, and manufacturing history.
Ideal candidate
A systems-minded builder who loves making complex hardware processes simple and reliable for operators, thriving at the intersection of software, hardware, and manufacturing.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or related field, or equivalent practical experience
- 5+ years of software engineering experience, preferably involving manufacturing, robotics, hardware test, embedded systems, or fleet operations
- Strong Python experience and familiarity with Linux systems, services, permissions, packaging, and process management
- Experience building reliable web applications and APIs used to orchestrate local or remote hardware
- Experience designing stateful workflows with retries, timeouts, resumability, cancellation, and failure recovery
- Familiarity with device provisioning, firmware flashing, OTA updates, calibration, or automated hardware testing
- Experience integrating with inventory, traceability, MES, ERP, or similar production systems
- Strong understanding of asynchronous jobs, idempotency, durable state, and distributed-system failure modes
- Experience with secure artifact distribution, authentication, authorization, and least-privilege hardware access
- Ability to diagnose issues across browsers, networks, operating systems, backend services, and physical devices
- Strong product judgment for technician-facing workflows and operational tooling
- Excellent cross-functional communication and project-management skills
Lab profile
About Physical Intelligence
Developing general-purpose AI foundation models to power robots for any physical task.
What they build
- General-purpose robot foundation models
- Learning algorithms for robot control
- Robotic systems and platforms
Known for
- Foundation model and system-building research
- Large-scale multimodal robot data operations
- Real-world fleet deployment and operations
- Prototype-to-production hardware engineering