Controls Engineer

physicalintelligence; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Physical Intelligence in San Francisco is seeking a Controls Engineer to design and implement model-based control algorithms for PI’s robotic systems, ensuring predictable, safe behavior in varied environments. You will build and optimize real-time control loops and bring-up arms, mobile bases, teleop systems, and full-body platforms.

Collaborate with researchers, hardware teams, and operators to validate models in simulation and translate them to robust hardware implementations, enabling

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
• 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

Responsibilities

• Design & implement control algorithms: PID, LQR, MPC, inverse dynamics, feedforward controllers.
• Build & validate models for simulation and control design.
• Develop real-time loops, incl. neural-network-driven control.
• Own robotic bring-up: integrate arms, bases, teleop systems, full-body platforms.
• Debug complex system behaviors using first-principles reasoning.
• Build sensor/actuator subsystems with embedded drivers and protocols.
• Partner cross-functionally with researchers, platform engineers, and operators to ensure stable real-world behavior.
• Support R&D by prototyping configurations and collecting structured datasets.

Skills

Control algorithms

Real-time systems

Python

C++

Embedded development

Diagnostics

Tools

CAN protocol

SPI protocol

I2C protocol

Ethernet

Job description

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

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.