Controls Engineer

Physical Intelligence; San Francisco (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

A robotics technology company in San Francisco is looking for a Controls Engineer to design and implement algorithms for their robots. The role involves developing control systems and integrating hardware components while collaborating with cross-functional teams. Ideal candidates should have a deep understanding of model-based control and proficiency in Python and C++. The position offers the chance to work on cutting-edge robotics technology in a dynamic environment.

Qualifications

• 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.

Responsibilities

• Design & implement control algorithms: PID, LQR, MPC.
• Develop real-time loops and optimize runtime control loops.
• Integrate and tune robotic systems including arms and mobile bases.

Skills

Model-based control algorithms

Python

C++

Real-time control loops

Debugging electromechanical systems

Communication protocols

Job 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 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

Bonus Points If You Have

• Background in manipulation or mobile robotic platforms
• Exposure to robot learning or integrating learned policies into control stacks
• Ability to design or refine custom actuator or sensor hardware