AI Engineer (RL & WBC)
About The Position
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 realtime 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.
- M.Sc. or Ph.D. in Robotics, Computer Science, Mechanical Engineering, or a related field.
Nice To Haves
- Experience with high-performance computing for distributed training.
- Contributions to open-source RL, ML or robotics projects.
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.
Benefits
- health
- vision
- dental
- 401k
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
