Controls Engineer

Physical Intelligence

  • San Francisco, CA
  • Today

    Highlights

    What We Hope You'll Bring Deep understanding of model-based control algorithms and inverse dynamicsAbility to validate control approaches in simulation and translate them to real hardwareProficiency in Python and C++, including firmware-adjacent developmentSkill in writing and tuning real-time control loopsHands-on capability to debug electromechanical systems end-to-endFamiliarity with embedded communication protocols (CAN, SPI, I2C, Ethernet)Clear communication with researchers, hardware teams, and operatorsA structured, collaborative approach to solving complex system issuesBonus Points If You Have Background in manipulation or mobile robotic platformsExposure to robot learning or integrating learned policies into control stacksAbility to design or refine custom actuator or sensor hardware#J-18808-Ljbffr. 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.

    Numbers & Facts

    LocationSan Francisco, CA

    Description

    Overview Who We Are 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 Controls Engineer, you will design and implement the algorithms that make PI's robots behave predictably, smoothly, and safely under varied and uncertain conditions.The Team 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. 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.In This Role You Will 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.Debug complex system behaviors: Diagnose and resolve hardware/software/runtime issues using first-principles reasoning.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.What We Hope You'll Bring Deep understanding of model-based control algorithms and inverse dynamicsAbility to validate control approaches in simulation and translate them to real hardwareProficiency in Python and C++, including firmware-adjacent developmentSkill in writing and tuning real-time control loopsHands-on capability to debug electromechanical systems end-to-endFamiliarity with embedded communication protocols (CAN, SPI, I2C, Ethernet)Clear communication with researchers, hardware teams, and operatorsA structured, collaborative approach to solving complex system issuesBonus Points If You Have Background in manipulation or mobile robotic platformsExposure to robot learning or integrating learned policies into control stacksAbility to design or refine custom actuator or sensor hardware#J-18808-Ljbffr

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