Machine Learning Engineer (f/m/d)

Hexagon Robotics is a division of Hexagon – a global leader in precision measurement. The division develops humanoid robots for industrial sectors to address labor shortages and accelerate the transition from automation to autonomy. Our first humanoid, AEON, was launched in June 2025 and is already in pilots with five customers. We are looking for a Machine Learning Engineer to develop and improve learning-based behaviors for our humanoid robots. You will work primarily on reinforcement learning (RL) and imitation learning (IL), training and iterating on policies, analyzing experiments, and addressing the gap between simulation and real-world robot performance. Your Mission Develop, train, and iterate on reinforcement learning policies for humanoid locomotion and manipulation Analyze training metrics and experiment results from simulation and real robots, identify issues, and propose follow-up actions Investigate sim-to-real gaps and develop approaches to improve the transfer of learned policies to real robots Develop machine learning solutions in Python using frameworks such as PyTorch, TensorFlow, or JAX Build and maintain RL/IL training pipelines, including environments, curricula, normalization, and evaluation Evaluate learning algorithms and adapt them to the specific problem at hand, with support from experienced team members Work collaboratively in a multicultural and interdisciplinary environment on a novel humanoid robot Proactively identify opportunities for improvement and contribute ideas to the team Your Skillset What we are looking for PhD/MSc in Robotics, Computer Science, or a related technical field Python programming skills and experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX Ability to independently write and debug basic C++ code Hands-on experience developing and training reinforcement learning policies. Experience with imitation learning or related learning-based methods is a plus Ability to interpret training metrics and experimental results and use them to guide the next iteration Understanding of sim-to-real challenges in robotics and approaches to address them Autonomous and proactive working style, with a strong drive to experiment, learn, and solve problem Nice to have Experience working with real robots Experience with modern RL/IL/ML approaches, such as transformers or cross-embodiment transfer learning Experience with Isaac Sim or other standard RL simulators Experience working independently with ROS2, including writing nodes and debugging interactions between them Experience defining objectives for your work and integrating them into a broader team plan or owning an epic What You’ll Get Flexible working hours and a hybrid model for real work-life balance CHF 500 mobility credit for sustainable commuting Bonus system