Lead Reinforcement Learning Engineer (Robotics)

Location: Zurich, Switzerland

Company

Develops cognitive humanoid robotics, including the 4NE1 platform and learning-based capabilities for commercial deployment.

What you will do

• Define the strategic roadmap for reinforcement learning, imitation learning, behavior cloning, and foundation-model-powered robotics.
• Lead the development and deployment of control policies for humanoid locomotion, loco-manipulation, and whole-body motion.
• Build scalable frameworks and quality standards for training, validating, and operating learning-based robotic systems.
• Industrialize research in offline learning, teleoperation, motion tracking, and robotics foundation models.
• Drive sim-to-real transfer through domain randomization, system identification, physics calibration, actuator modeling, and sensor alignment.
• Lead, mentor, and grow a team while collaborating with Controls, Perception, Platform, Software, Hardware, product, and executive teams.

Requirements

• Master’s or Ph.D. in Robotics, Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 8+ years of experience in robotics, reinforcement learning, or machine learning, including technical leadership experience.
• Track record of building and deploying learning-based robotic systems in production and leading multidisciplinary teams.
• Strong knowledge of robot dynamics and kinematics, whole-body control, motion planning, sensor integration, and sim-to-real transfer.
• Hands-on experience with Isaac Lab, Isaac Sim, MuJoCo, PyTorch, ROS2, Python, and C++.
• Excellent English communication skills required.

Nice to have

• German language skills.
• Experience bridging academic research and industrial deployment.
• Strategic planning, stakeholder management, mentoring, and ownership of ambitious projects through completion.

Culture & Benefits

• Work on next-generation humanoid robots and commercially deployable cognitive robotics.
• Collaborate across research, engineering, product, and hardware disciplines.
• Promote technical excellence, ownership, collaboration, scientific rigor, and innovation.
• Shape long-term technology strategy and future generations of humanoid platforms.