How will you be part of the team? 

• Develop and implement locomotion control algorithms for bipedal walking, including balance control, gait planning, and reactive adaptation.
• Design and optimize real-time controllers for dynamic stability, disturbance rejection and energy efficiency.
• Integrate sensor feedback (IMUs, force/torque sensors, cameras, etc.) for state estimation and closed-loop control.
• Simulate, test and validate walking controllers in both simulation and real-world environments.
• Collaborate with hardware, perception and planning teams to refine system performance and robustness.
• Contribute to the development of whole-body control strategies for humanoid motion.

What does an ideal background look like?

• MS or PhD in Robotics, Mechanical Engineering, Computer Science, or a related field.
• 5+ years of experience in bipedal locomotion control, legged robotics, and dynamic systems.
• Expertise in model-based control techniques (e.g., inverse dynamics, whole-body control, MPC).
• Strong background in kinematics, dynamics, and optimization for robotic motion.
• Proficiency in C++ and/or Python for real-time control implementation.
• Experience with physics-based simulation tools (e.g., MuJoCo, Gazebo, PyBullet, Isaac, etc.).
• Familiarity with ROS, LCM, or other middleware for robotic systems.

What are additional skills that would make a candidate stand out?

• Experience with reinforcement learning or data-driven approaches for locomotion.
• Significant hands-on experience deploying control algorithms on real humanoid robots in real world settings.
• Knowledge of hardware actuation, compliance control, and force-based interactions.
• Understanding of energy-efficient locomotion strategies for long-duration operation.