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XPENG

Staff Reinforcement Learning Engineer - Robotics

๐Ÿ“ŒSanta Clara, United States ๐Ÿ‡บ๐Ÿ‡ธ

โฑ๏ธŽ full-time

๐Ÿง™โ€โ™‚๏ธ mid-level

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity. Job Responsibilities include researching, implementing, and evaluating deep-learning-based methods for legged locomotion and whole-body control problems in humanoid robots; developing and refining end-to-end robot motion controllers using reinforcement learning, imitation learning, or other advanced techniques; designing, executing, and analyzing experiments to evaluate RL controllers and address sim-to-real challenges; and staying updated and integrating the latest advancements in academic and engineering research for humanoid robotics. Minimum Requirements include an advanced degree in Mechanical Engineering, Computer Science, Robotics, or a related field; proficiency in Python and strong software design skills; 3-5+ years of experience with deep learning frameworks like PyTorch; strong understanding of reinforcement learning and imitation learning techniques; and proven experience applying algorithms such as PPO, DQN, SAC, etc., to real-world problems. Preferred Requirements include experience with C++ and hands-on experience with the control and operation of legged robot hardware. The company provides a fun, supportive and engaging environment, opportunities to make a significant impact on the future of transportation and robotics, opportunity to work on cutting edge technologies with the top talent in the field, competitive compensation package & benefits, snacks, lunches, and fun activities. XPENG is an Equal Opportunity Employer.
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  • Employment

    โฑ๏ธŽ full-time

  • Experience

    ๐Ÿง™โ€โ™‚๏ธ mid-level

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