robot learning research

HireHi; Greater London; On-site; GBP 27,000 - 41,000; Full time

Benefits offered by this job

Ежедневный завтрак

Обеды и закуски в офисе

Доступ к руководству

Job summary

Humanoid ищет талантливого стажёра на 12–24 недели в Лондоне. Вы будете работать над обучением политик манипуляций в реальном мире, моделями мира и переносом политики из симуляций в жизнь, а также над оптимизацией обучения на реальном оборудовании.

Требования: магистр/PhD в CS/ML/робототехнике, сильные основы ML, Python, PyTorch или JAX, интерес к RL и мир-моделям, умение анализировать результаты и быстро учиться.

Qualifications

• Проводится обучение в области машинного обучения и робототехники.
• Сильные основы в машинном обучении.
• Опыт работы с PyTorch или JAX.

Responsibilities

• Обучение политик манипуляций с использованием обучения с подкреплением в реальном мире.
• Создание разнообразных наборов задач и моделей RL в симуляции (Isaac Sim, MuJoCo).
• Эксперименты по переносу политик из симуляции в реальный мир.
• Разработка предиктивных моделей видеоматериалов и динамических моделей.
• Использование world models как симуляторов для офлайн-оценки политик.

Skills

Python

PyTorch

JAX

Machine learning

Reinforcement learning

Experimentation

Fast learner

Education

Master’s degree or PhD in CS/ML/Robotics

Job description

Описание:

Humanoid is developing commercially scalable, safe humanoid robots, including the HMND-01 platform for deployment in real industrial environments. It builds software systems that enable robots to operate effectively in the real world and expand human capabilities.

Задачи:

• Train language-vision conditioned manipulation policies using reinforcement learning in the real world
• Construct challenging and diverse manipulation task suites and reinforcement learning models in simulation using Isaac Sim and MuJoCo
• Experiment with transferring policies trained in simulation to the real world
• Develop action-conditioned video prediction and physically consistent dynamics models over long horizons
• Use world models as learned simulators to score candidate policies offline and generate synthetic rollouts for training
• Build fidelity metrics that quantify where world models can be trusted
• Explore in-context learning, short- and long-term memory, and post-train VLA models for production-grade use cases
• Work with different data modalities, close the embodiment gap between human and robot data, and improve data diversity and attribution
• Optimise models for real-time edge inference on robot hardware, including profiling, quantisation, and latency/throughput trade-offs
• Improve training and data-loading performance across distributed GPU infrastructure

Требования:

• Pursuing or holding a master’s degree or PhD in computer science, machine learning, robotics, or a related field
• Strong foundations in machine learning
• Strong Python skills and hands-on experience with PyTorch or JAX
• Interest in one or more of reinforcement learning, world models and generative video, VLA/multimodal models, or ML systems and inference optimisation
• Experience running experiments and interpreting results rigorously
• Ability to take ownership and iterate with guidance
• Strong problem-solving skills and attention to detail
• Ability to learn quickly and work in a research-driven, fast-moving environment

Условия:

• Internship for 12 to 24 weeks, 5 days per week
• Flexible start date
• Competitive pay and perks
• Free daily breakfast, catered lunch, and snacks in the office
• Daily collaboration with engineers, researchers, and product experts building AI and humanoid robotics
• Direct access to founding leadership, input on product direction, and the ability to drive initiatives from day one