Controls Engineer
Physical Intelligence · San Francisco
CHECKED 13H AGO
Roboticsonsite
At a glance
Highlights
- Design control algorithms for robots to behave predictably and safely.
- Own bring-up and tuning of arms, mobile bases and full-body platforms.
- Ensure learning-based systems run reliably under real-time constraints.
Ideal candidate
A hands-on roboticist who loves first-principles debugging and collaborating across research and hardware. You care about stable, predictable real-world robot behavior and thrive tuning complex systems in unpredictable environments.
Requirements
- Deep understanding of model-based control algorithms and inverse dynamics
- Ability to validate control approaches in simulation and translate them to real hardware
- Proficiency in Python and C++, including firmware-adjacent development
- Skill in writing and tuning real-time control loops
- Hands-on capability to debug electromechanical systems end-to-end
- Familiarity with embedded communication protocols (CAN, SPI, I2C, Ethernet)
- Clear communication with researchers, hardware teams, and operators
- A structured, collaborative approach to solving complex system issues
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