Hybrid RL/IL Engineer for Humanoid Robots (Sim-to-Real)

Hexagon Robotics; Zürich; Hybrid; CHF 110,000 - 160,000; Full time

Benefits offered by this job

Hybrid model

Mobility credit

Bonus system

Pension contributions

Training programs

Relocation support

Health discounts

Team events

Flat hierarchy

International culture

Job summary

Hexagon Robotics, a division of Hexagon and a global leader in precision measurement, seeks a Machine Learning Engineer to advance reinforcement and imitation learning for humanoid robots. You will develop, train, and iterate policies, analyze experiments, and address sim-to-real gaps to improve real-world robot performance.

You’ll work in Python with PyTorch, TensorFlow or JAX, build end-to-end training pipelines, and collaborate in a multicultural, interdisciplinary team.

Qualifications

• 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

Responsibilities

• 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

Skills

PhD/MSc robotics

Python ML frameworks

C++ programming

RL/IL experience

Experiment analysis

Sim-to-real knowledge

Autonomous working style

Education

PhD/MSc Robotics/CS

Tools

PyTorch

TensorFlow

JAX

Job description

Hexagon Robotics, a division of Hexagon and a global leader in precision measurement, seeks a Machine Learning Engineer to advance reinforcement and imitation learning for humanoid robots. You will develop, train, and iterate policies, analyze experiments, and address sim-to-real gaps to improve real-world robot performance.

You’ll work in Python with PyTorch, TensorFlow or JAX, build end-to-end training pipelines, and collaborate in a multicultural, interdisciplinary team.