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.
