Why are We Hiring for this Role: Design, develop, and optimize reinforcement learning algorithms with world models 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. Analyze and optimize control performance with a focus on robustness, energy efficiency, and adaptability. Contribute to the continuous development of our in-house training pipelines and tooling. What Kind of Person We Are Looking For: Proficiency in Python and C++ for algorithm development and deployment. 2 years of experience in machine learning (NNs, LVMs, VLAs) and reinforcement learning applied to robotics or similar real-time environments. Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Lab). Experience with deep learning frameworks (e.g., PyTorch, JAX, TensorFlow). Strong understanding of classical and modern control theory, locomotion dynamics, etc. Experience working with physical robots. Contributions to open-source ML or robotics projects. M.Sc. or Ph.D. in Robotics, Computer Science, Mechanical Engineering, or a related field. Publication record in the field.