Physical Intelligence는 로봇의 안정성과 신뢰성을 높일 Controls Engineer를 채용합니다. 모델 기반 제어 알고리즘 설계 및 Python/C++를 활용한 실시간 제어 루프 개발이 핵심입니다. 로봇 하드웨어 통합 및 디버깅 경험이 필수이며, 연구원들과 협업하여 로봇의 동작을 최적화하는 역할을 수행합니다.
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 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.
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.
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
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.
