4 days ago
London, United KingdomSenior
Responsibilities
- Design and train learning-based locomotion and whole-body control policies for dynamic robotic behaviors.
- Develop composable motion primitives using learning-based generative models.
- Apply reinforcement learning, imitation learning, and related approaches to motion generation and control.
- Build scalable simulation, data-generation, and training pipelines.
- Develop robustness, adaptation, domain-randomization, and sim-to-real-transfer methods.
- Deploy, evaluate, debug, and fine-tune learned policies on physical humanoid robots.
- Optimize policy inference and training systems for real-time control performance and latency.
- Integrate learned policies into the robot control and autonomy stack.
Requirements
- BS, MS, or PhD in Robotics, Machine Learning, Computer Science, or a related technical field.
- At least 3 years of experience 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 reinforcement-learning experience plus experience in imitation learning, generative models, sequence models, or large-scale representation learning.
- Strong Python and PyTorch experience.
- Experience with physics-based simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or similar platforms.
- Ability to reason about learning algorithms in the context of dynamics, control, and physical systems.
- Preferred experience applying learning to legged robots, humanoids, or other complex physical systems; whole-body control; locomotion; motion imitation; loco-manipulation; real-robot deployment; motion capture; trajectory optimization; model-based control; learning from demonstrations; large-scale simulation; or relevant publications and open-source contributions.
Benefits
- Competitive equity through stock options with meaningful upside.
- 30+ paid days off, including 23 days of annual leave, UK bank holidays, and company closure days including the Christmas–New Year shutdown.
- Private healthcare with virtual and in-person care.
- Pension scheme with an 8% total contribution, consisting of 5% employee and 3% employer contributions, on full earnings.
- Free daily breakfast, catered lunch, and snacks in the office.
- In-office collaboration with engineers, researchers, and product experts.
- Direct access to founding leadership and meaningful input into product direction.
Categories
ML EngineeringRobotics
About Humanoid
Humanoid develops general-purpose humanoid robots for industrial environments to automate hazardous or monotonous tasks in manufacturing, logistics, and construction. Its HMND‑01 Alpha platform is running in real industrial pilots, pointing toward a hardware-and-software product paired with deployment services. Founded in 2024 and headquartered in London, the privately held company is scaling toward wider commercialization from these pilots.