Reinforcement Learning Engineer - Manipulation

Humanoid is seeking a top-tier Reinforcement Learning Engineer specializing in complex robotic manipulation tasks. This role requires mastery in using advanced RL techniques to teach the humanoid system how to interact safely and effectively with its dynamic environment. Key responsibilities include designing novel control policies for various tools and end effectors, simulating grasp mechanics, and optimizing sequential decision-making processes. You will work on projects ranging from intricate object manipulation (e.g., picking up specific items) to collaborative tasks requiring human-robot interaction awareness. Experience with biomechanical models of the arm/hand and robust policy generalization is essential. The successful candidate must be adept at scaling RL approaches from simulation to physical hardware, demonstrating a deep understanding of kinematic and dynamic constraints.

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