Robot Learning Research Intern
UK-based AI and robotics company building commercially scalable humanoid robots for industrial deployment.
Funding history
About Humanoid
Humanoid develops industrial humanoid robots and its proprietary KinetIQ AI framework for real-world tasks across manufacturing, logistics, and related environments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will contribute to robot-learning research across reinforcement learning, world models, pretraining, and inference optimisation. You may train manipulation policies, build simulation tasks and generative models, evaluate experiments, optimise edge inference, and improve distributed GPU training and data-loading performance.
Requirements
- Pursuing or holding a master’s or PhD in computer science, machine learning, robotics, or a related field
- Machine learning foundations
- Python proficiency
- Hands-on experience with PyTorch or JAX
- Interest in reinforcement learning, world models, generative video, VLA or multimodal models, or ML systems and inference optimisation
- Experience running experiments and interpreting results rigorously
- Problem-solving skills and attention to detail
Responsibilities
- Train language-vision-conditioned manipulation policies using reinforcement learning
- Construct manipulation task suites and reinforcement-learning models in simulation
- Experiment with sim-to-real policy transfer
- Develop action-conditioned video prediction and dynamics models
- Use world models for policy scoring and synthetic training rollouts
- Build world-model fidelity metrics
- Post-train VLA models for production use cases
- Optimise models for real-time edge inference
- Improve training and data-loading performance across distributed GPU infrastructure
Benefits
- Free daily breakfast, catered lunch, and snacks in-office
Hiring Process
Complete the intern challenge and submit a solution as a public GitHub repository with a README or presentation by Friday, 9 October 2026, 23:59 BST; include the repository URL, name, and CV in the application form.