Senior RL Engineer, Humanoid Robotics & Motion Control

Xpengmotors; Santa Clara (CA); On-site; USD 174,720 - 295,680; Full time

Benefits offered by this job

Fun environment

Impact on transportation and robotics

Cutting-edge technologies

Competitive compensation package

Snacks and lunches

Job summary

XPENG is a leading smart tech company advancing AI-driven humanoid robotics and autonomous systems. This role focuses on researching and implementing deep-learning methods for legged locomotion and whole-body control, with emphasis on RL and imitation learning for real-world robots.

You will design experiments, analyze results, and integrate the latest academic breakthroughs to stay at the forefront of robotics research and development.

Qualifications

• Advanced degree in Mechanical Engineering, Computer Science, Robotics, or related field.
• 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.
• Experience applying algorithms such as PPO, DQN, SAC to real-world problems.

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.

Skills

Python

Reinforcement learning

Imitation learning

Research

Communication

Education

Advanced degree in Mechanical Engineering, Computer Science, Robotics, or related field

Tools

PyTorch

C++

Job description

XPENG is a leading smart tech company advancing AI-driven humanoid robotics and autonomous systems. This role focuses on researching and implementing deep-learning methods for legged locomotion and whole-body control, with emphasis on RL and imitation learning for real-world robots.

You will design experiments, analyze results, and integrate the latest academic breakthroughs to stay at the forefront of robotics research and development.