Current opportunities.
The opportunity
Join a cutting-edge humanoid robotics team to develop and implement learning-based algorithms for dynamic locomotion and whole-body control. Work with interdisciplinary engineers to create robust, reusable motion capabilities that enable advanced robot movement and interaction in real industrial settings.
What you could work on
- Design and train learning-based locomotion and whole-body control policies for dynamic robotic behaviors
- Develop motion primitives using learning-based generative models
- Apply reinforcement learning, imitation learning, and other learning-based approaches to motion generation and control
- Build scalable simulation, data generation, and training pipelines using platforms like Isaac Lab and MuJoCo
- Develop methods for robustness, adaptation, and sim-to-real transfer including domain randomization and learning from real-world robot data
- Deploy, evaluate, debug, and fine-tune learned policies on physical humanoid robots
- Optimize policy inference and training systems for performance and latency in real-time control
- Integrate learned policies into the robot control and autonomy stack
Relevant experience
- Degree in Robotics, Machine Learning, Computer Science, or related technical field (BS, MS, or PhD)
- 3+ years developing machine learning systems in industry or research
- Strong understanding of deep learning and modern policy architectures including training, inference, experimentation, and debugging
- Hands-on experience with reinforcement learning and at least one additional area such as imitation learning, generative models, sequence models, or large-scale representation learning
- Proficiency in Python and PyTorch
- Experience with physics-based simulation environments such as Isaac Lab, Isaac Sim, or MuJoCo
- Ability to apply learning algorithms in the context of dynamics, control, and physical systems
Security vetting
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Working arrangements
London, On-site. Confirm employment terms and any clearance requirements with your consultant.