We are searching for a Humanoid Robotic Engineer specialized in bipedal locomotion and manipulation. This role involves designing and implementing sophisticated control algorithms, with a focus on Reinforcement Learning, to enhance the performance of robotic systems both in simulation and real-world applications.

• Develop and implement advanced control algorithms for bipedal locomotion and manipulation.
• Train Reinforcement Learning (RL) policies in simulation and deploy them on physical hardware.
• Conduct sim-to-real transfer and analyze gait/kinematics telemetry.

• Strong background in Model Predictive Control (MPC) and Whole-Body Control.
• Extensive experience with robotic simulation environments (e.g., NVIDIA Isaac Sim, MuJoCo).
• Familiarity with modern RL frameworks.