Your Role \& Responsibilities
Design and develop state-of-the-art machine learning methods for whole-body control of next-generation humanoid robotic systems
Build scalable training pipelines for reinforcement learning and imitation learning at industrial scale
Collaborate closely with robotics control engineers, perception teams, and software developers to integrate ML-driven control into a complex real-world robot stack
Contribute across the full development lifecycle — from early research prototypes to robust, production-ready controllers
Deploy ML-based controllers on physical humanoid robots and iterate through hands-on testing, evaluation, and optimization
Provide technical leadership, mentorship, and guidance within a multidisciplinary R\&D environment
Join an agile, fast-moving team at the intersection of robotics, autonomy, and advanced industrial technology
Required Technical \& Professional Expertise
MS or PhD in Computer Science, Robotics, Mechanical Engineering, or a related field
5+ years of experience developing and deploying machine learning models, ideally in robotics or control systems
Deep expertise in reinforcement learning, imitation learning, or learning-based control for robotic platforms
Proven experience deploying ML-based control methods on robotic hardware
Strong proficiency with ML frameworks such as PyTorch, TensorFlow, or JAX
Solid programming skills in Python; experience with C++ is an advantage
Familiarity with robotics frameworks and tools (e.g., ROS2, Omniverse) and the ability to collaborate closely with cross-functional robotics teams
Proficiency in English; basic German skills are a plus