Artificial Intelligence Engineer (RL & WBC)

About the role

Design, develop, and optimize reinforcement learning algorithms for real-time control and locomotion of humanoid robots.
Integrate learned policies into real-world robot platforms with hardware-in-the-loop validation.
Collaborate with mechanical, perception, and embedded systems teams to ensure tight integration between hardware and software.
Apply advanced techniques such as curriculum learning, domain randomization, and sim2real transfer to improve policy generalization.
Analyze and optimize control performance with a focus on robustness, energy efficiency, and adaptability.
Contribute to the continuous development of our in-house RL training pipelines and tooling.

Responsibilities

• Design, develop, and optimize reinforcement learning algorithms for real-time control and locomotion of humanoid robots.
• Integrate learned policies into real-world robot platforms with hardware-in-the-loop validation.
• Collaborate with mechanical, perception, and embedded systems teams to ensure tight integration between hardware and software.
• Apply advanced techniques such as curriculum learning, domain randomization, and sim2real transfer to improve policy generalization.
• Analyze and optimize control performance with a focus on robustness, energy efficiency, and adaptability.
• Contribute to the continuous development of our in-house RL training pipelines and tooling.

Requirements

• 2+ years of experience in machine learning (NNs, LVMs) and reinforcement learning applied to robotics or similar real-time environments.
• Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Lab).
• Proficiency in Python and C++ for algorithm development and deployment.
• Experience with deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).
• Familiarity with ROS/ROS2 and real-time robotic systems.
• Strong software development experience, including CI/CD, unit testing, etc.
• Strong understanding of classical and modern control theory, locomotion dynamics, etc.
• Experience deploying RL algorithms on physical robots.
• Experience with high-performance computing for distributed training.
• Contributions to open-source RL, ML or robotics projects.
• M.Sc. or Ph.D. in Robotics, Computer Science, Mechanical Engineering, or a related field.

Qualifications

• Proven record of exceptional ability and a history of creating things that work.
• Diverse perspectives from various industries and fields.

Skills

• Machine Learning (NNs, LVMs)
• Reinforcement Learning
• Physics Simulation Environments (MuJoCo, Isaac Lab)
• Python and C++
• Deep Learning Frameworks (PyTorch, JAX, TensorFlow)
• ROS/ROS2 and Real-time Robotic Systems
• CI/CD, Unit Testing
• Classical and Modern Control Theory, Locomotion Dynamics
• Deployment of RL Algorithms on Physical Robots
• High-Performance Computing for Distributed Training
• Open-source Contributions to RL, ML or Robotics Projects

Benefits

• Flexible Work Environment
• Competitive Compensation
• Professional Development Opportunities
• Work-Life Balance

Pay

• $100,000 - $150,000 annually

Schedule

• Full-Time