Senior Reinforcement Learning Engineer - Robotics

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.

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.

Experience with C++ is a plus.

Hands-on experience with the control and operation of legged robot hardware is highly preferred.

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.