Senior Reinforcement Learning Engineer - Robotics

XPENG • Santa Clara, CA

10d • $174,720 - $295,680

About The Position

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.

Requirements

• Advanced degree in Mechanical Engineering, Computer Science, Robotics, or a related field. Open to fresh graduates.
• Proficiency in Python and strong software design skills.
• 1-3+ years of experience with deep learning frameworks like PyTorch.
• Strong understanding of reinforcement learning and imitation learning techniques.
• Proven experience applying algorithms such as PPO, DQN, SAC, etc., to real-world problems.

Nice To Haves

• Experience with C++ is a plus.
• Hands-on experience with the control and operation of legged robot hardware is highly preferred.

Responsibilities

• Research, implement, and evaluate deep-learning-based methods for legged locomotion and whole-body control problems in humanoid robots.
• Develop and refine end-to-end robot motion controllers using reinforcement learning, imitation learning, or other advanced techniques.
• Design, execute, and analyze experiments to evaluate RL controllers and address sim-to-real challenges.
• Stay updated and integrate the latest advancements in academic and engineering research for humanoid robotics.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior