Physical Intelligence
Controls Engineer
San Francisco
Sponsorship not specifiedDetected 282 days ago
PythonC++AlgorithmsEmbedded SystemsResearchCommunication
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odds of building a lasting career here
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Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- They work closely with research, hardware, and operations to debug complex system behaviors and ensure our learning-based systems operate under strict real-time constraints in unpredictable environments.
Responsibilities
- Design & implement control algorithms: PID, LQR, MPC, inverse dynamics, and feedforward controllers.
- Build & validate models: Create and refine physical and inverse dynamics models for simulation and control design.
- Develop real-time loops: Write and optimize runtime control loops, including neural-network-driven control.
- Own robotic bring-up: Integrate and tune arms, mobile bases, teleop systems, and full-body platforms.
- Build sensor/actuator subsystems: Work with embedded systems, drivers, and communication protocols (CAN, SPI, I2C, Ethernet).
- Partner cross-functionally: Work with researchers, platform engineers, and operators to ensure stable, predictable real-world behavior.
- Support R&D: Prototype configurations, collect structured datasets, and iterate directly with researchers.
- Ability to validate control approaches in simulation and translate them to real hardware
- Ability to design or refine custom actuator or sensor hardware
Nice to have
- Deep understanding of model-based control algorithms and inverse dynamics
- 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
- Background in manipulation or mobile robotic platforms
Benefits
- Bonus Points If You Have
- Exposure to robot learning or integrating learned policies into control stacks
Company info
- The Controls team builds and tunes the core feedback and model-based algorithms, real-time loops, simulations, and actuator/sensor subsystems that make PI's robots stable and reliable.
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