Description
We are seeking a Senior Controls Engineer to design, implement, and deploy cutting-edge control algorithms for dynamic legged robotic systems operating in complex real-world environments. You will work across dynamics, optimization, state estimation, and real-time software owning both algorithmic innovation and system-level performance on hardware. This role is ideal for engineers who thrive on high-velocity problem solving, deep technical ownership, and hands-on testing and validation.
Responsibilities
- Design, implement, and validate advanced control architectures (e.g., model-based, optimization-based, learning-augmented controllers) for agility and robustness.
- Develop, maintain, and validate state estimation and sensor fusion pipelines (IMU, joint encoders, contact/force sensing).
- Lead gait generation, footstep planning, contact scheduling, and disturbance recovery tuning.
- Perform rigorous offline and real-time testing in simulation and hardware environments.
- Debug and analyze system performance using logs, visualization tools, hardware experiments, and fleet data.
- Build automated diagnostics, analysis scripts, and tools to improve robot reliability and field performance.
- Collaborate closely with mechanical, perception, embedded, and systems teams to ensure end-to-end performance and robustness.
- Write clean, maintainable, real-time-safe code in C++ and Python.
- Mentor junior engineers and contribute to long-term architectural decisions.
Requirements
Required Qualifications
- Strong foundations in control theory (linear, nonlinear, optimal control) with experience in legged locomotion or other dynamic systems.
- Experience with multi-body dynamics, modeling, and simulation (e.g., MuJoCo, Gazebo, Isaac Sim, PyBullet).
- Hands-on experience deploying algorithms on physical robotic systems and debugging complex hardware/software interactions.
- Proficiency in modern C++ (C++17/20) and Python for development and tooling.
- Experience with Unix/Linux environments and software engineering best practices (version control, CI/CD).
- Masters/PhD in Robotics, Mechanical, Electrical, Aerospace Engineering or equivalent work experience.
Preferred Qualifications
- Experience with legged or humanoid robots and real-world locomotion challenges.
- Background in whole-body control frameworks (operational space control, MPC, etc.).
- Familiarity with state estimation methodologies (EKF, factor graphs, UKF).
- Experience architecting data analysis pipelines and automated diagnostic systems.
- Publications or significant open-source contributions in robotics, controls, or estimation.
- Experience with ROS 2 and real-time middleware.
- A combination of classical control and reinforcement learning applied to robotic systems.
- Demonstrated ability to lead technical efforts and mentor junior engineers.
