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.