Requirements:

A profound understanding of the theoretical foundations of machine learning, reinforcement learning, or robot learning
Deep expertise in at least one relevant area, such as reinforcement learning, imitation learning, multimodal generative modeling, computer vision, robotics, planning, or control
Experience training and evaluating modern deep learning models, including transformer-based or multimodal foundation models
Substantial experience training large models across multiple computational nodes
Strong software engineering and algorithm-design skills; we primarily use Python
Deep experience with a modern deep learning framework; we primarily use JAX
Experience designing, executing, and analyzing machine learning experiments with appropriate statistical rigor
Ability to formulate meaningful research questions, design experiments that test clear hypotheses, and draw defensible conclusions
Experience implementing research ideas and iterating quickly across modeling, data, infrastructure, and evaluation
Strong communication and leadership abilities, including the ability to collaborate across research and engineering disciplines
Ability to document research findings clearly and contribute to technical reports or research publications

Nice to have:

Experience working with real-world robots and robotic simulation environments
Experience with dexterous manipulation, whole-arm manipulation, mobile manipulation, or humanoid robotics
Experience with multimodal sensing, including tactile, force-torque, depth, and proprioceptive signals
Experience collecting human demonstrations through teleoperation, motion capture, wearable devices, or observation
Experience developing or post-training vision-language models, vision-language-action models, or video and world models
Experience with deep reinforcement learning techniques
Familiarity with robotics tools and simulators
Knowledge of scalable training techniques
A PhD in Computer Science, Robotics, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience
A track record of impactful publications, open-source contributions, or deployed robotic systems
Experience engineering large distributed data-processing, simulation, or model-training systems
A record of building and delivering products or research prototypes in a dynamic, startup-like environment
Passion for moving research from controlled experiments to capable, reliable real-world robotic systems
Excellent command of English, with strong technical writing, presentation, and communication skills
Proficiency in contemporary software engineering practices

Designing, implementing, training, and evaluating large models and learning algorithms for robotic agents
Developing vision-language-action architectures that connect multimodal perception and language understanding with physical control
Investigating reinforcement learning and imitation learning methods for sparse, delayed, or difficult-to-verify objectives
Building scalable methods for incorporating demonstrations, teleoperation data, video, simulation trajectories, and autonomous robot experience into foundation models
Designing capture methodologies, datasets, evaluation protocols, and data-quality pipelines for embodied learning
Developing simulation environments and conducting sim-to-real experiments on physical robotic platforms
Exploring planning, guided generation, and search over action trajectories
Prototyping new capabilities in areas such as dexterous manipulation, mobile manipulation, and whole-body control
Writing robust research software and distributed training infrastructure that enable rapid experimentation
Collaborating with research and engineering teams to translate promising ideas into reliable real-world systems
Communicating results through technical reports, open-source releases, demonstrations, and research publications