Senior Reinforcement Learning Engineer - Robotics

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++ (plus).
• Hands-on experience with the control and operation of legged robot hardware (highly preferred).

Benefits

• 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.

Pay

The base salary range for this full-time position is $174,720 - $295,680, in addition to bonus, equity and benefits.

Schedule

Full-time position.

Qualifications

None specified.

Skills

Deep learning, reinforcement learning, imitation learning, Python programming, robotics, humanoid robots, legged locomotion, whole-body control.

Benefits

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.