Senior ML Research Engineer - Humanoid Robotics

About the Company

An innovative robotics company is developing the next generation of intelligent humanoid robots, combining cutting-edge AI, machine learning, and advanced engineering to bring autonomous systems into the real world.

With an ambitious vision and a highly skilled team of researchers and engineers, the company is pushing the boundaries of embodied AI, robotic locomotion, and intelligent automation.

This is an exciting opportunity to join a rapidly evolving field and contribute to technology that could transform how robots interact with the physical world.

About the Role

We’re looking for a Senior Machine Learning Research Engineer to develop the learning-based algorithms that enable humanoid robots to move, balance, and interact with their environments.

You’ll work at the intersection of reinforcement learning, robotics, and control systems, transforming advanced research into real-world capabilities deployed on physical humanoid robots.

Working closely with robotics, simulation, and autonomy engineers, you'll play a key role in developing intelligent motion and whole-body control systems.

Key Responsibilities

• Design and train reinforcement learning and imitation learning policies for robotic locomotion and whole-body control.
• Develop intelligent motion capabilities that enable humanoid robots to walk, balance, adapt, and interact with their surroundings.
• Build scalable simulation and training environments using Isaac Lab, Isaac Sim, MuJoCo, or similar platforms.
• Improve sim-to-real transfer, ensuring learned behaviours perform reliably on physical robots.
• Deploy, evaluate, and optimise ML policies for real-time robotic control.
• Collaborate with engineering teams to integrate learning-based algorithms into robotic systems.

Requirements

• 3+ years of experience in machine learning research or engineering, ideally involving robotics or physical systems.
• Strong programming experience with Python and PyTorch.
• Hands-on experience with reinforcement learning, alongside imitation learning, generative models, or related approaches.
• Experience with physics-based simulation environments such as Isaac Lab, Isaac Sim, or MuJoCo.
• Strong understanding of deep learning, policy architectures, and model training.
• Degree in Robotics, Machine Learning, Computer Science, or a related technical field.

Desirable Experience

• Experience with humanoid robotics, legged locomotion, or whole-body control.
• Knowledge of sim-to-real transfer, domain randomisation, and motion imitation.
• Experience deploying learned policies onto physical robotic systems.
• Familiarity with motion capture, trajectory optimisation, or model-based control.
• Experience scaling reinforcement learning training across simulated environments.
• Publications at NeurIPS, ICML, ICLR, CoRL, RSS, or contributions to relevant open-source projects.