Senior Reinforcement Learning Engineer - Robotics

XPENG; Santa Clara (CA); On-site; USD 175,000 - 296,000; Full time

Benefits offered by this job

Competitive compensation package

Bonus opportunities

Snacks & activities

Impact on transportation and robotics

Job summary

XPENG is hiring for a robotics-focused role building deep-learning based control for humanoid robots. You will research, implement, and evaluate legged locomotion methods and end-to-end motion controllers using reinforcement and imitation learning.

Candidates should have an advanced degree and strong Python/PyTorch skills, plus experience with RL algorithms (PPO, DQN, SAC). Opportunity to work on cutting-edge robotics at XPENG.

Qualifications

• Advanced degree in ME, CS, or robotics or related field.
• Proficiency in Python and deep learning frameworks.
• 1–3+ years in deep learning and RL applications.

Responsibilities

• Research, implement, and evaluate deep-learning methods for legged locomotion and control.
• Develop end-to-end robot motion controllers using RL or related methods.
• Design, execute and analyze experiments to evaluate RL controllers.
• Incorporate latest research advances in humanoid robotics.

Skills

Python

Reinforcement learning

Imitation learning

PPO

DQN

SAC

Education

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

Tools

PyTorch

C++

Job description

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

• 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.

Minimum 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.

Preferred Requirements

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

What Do We Provide

• 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.

The base salary range for this full-time position is $174,720 - $295,680, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.