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.

• 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.