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Controls Engineer at Physical Intelligence | San Francisco

On-site Full-time

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Experience Level

Experience

Qualifications

QualificationsIn-depth knowledge of model-based control algorithms and inverse dynamics. Experience in validating control strategies through simulation and translating them to physical hardware. Strong problem-solving skills and a passion for robotics and control systems. Excellent communication skills for effective collaboration across diverse teams.

About the job

About Us

At Physical Intelligence, we are at the forefront of integrating general-purpose AI into the physical realm. Our talented team comprises engineers, scientists, roboticists, and innovators dedicated to creating foundational models and advanced learning algorithms to enhance the robots of today and shape the physically-actuated devices of tomorrow.

Your Role

As a Controls Engineer, you will be pivotal in designing and implementing the algorithms that ensure our robots operate smoothly, predictably, and safely in diverse and uncertain environments.

The Controls Team

Our Controls Team is responsible for developing and fine-tuning essential feedback and model-based algorithms, real-time processing loops, simulations, and actuator/sensor subsystems that contribute to the stability and reliability of our robots. Collaborating closely with research, hardware, and operations teams, you will troubleshoot complex system behaviors and ensure that our learning-based systems function under rigorous real-time constraints in unpredictable settings.

Key Responsibilities

  • Design and Implement Control Algorithms: Develop PID, LQR, MPC, inverse dynamics, and feedforward controllers.
  • Model Development and Validation: Create and refine physical and inverse dynamics models for simulations and control design.
  • Real-Time Loop Development: Write and optimize runtime control loops, including neural network-driven control.
  • Robotic Integration: Integrate and tune robotic arms, mobile bases, teleoperation systems, and full-body platforms.
  • Debugging System Behaviors: Diagnose and address hardware, software, and runtime issues using first-principles reasoning.
  • Sensor/Actuator Subsystem Development: Collaborate with embedded systems, drivers, and communication protocols (CAN, SPI, I2C, Ethernet).
  • Cross-Functional Collaboration: Partner with researchers, platform engineers, and operators to ensure consistent and reliable real-world performance.
  • Support Research and Development: Prototype configurations, gather structured datasets, and work closely with researchers to iterate on solutions.

About Physical Intelligence

Physical Intelligence is pioneering the integration of AI into the physical world, driving innovation in robotics and actuated devices through cutting-edge technology and research.

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