Senior Research Scientist, Large Behavior Models

Responsibilities

Work as part of a dynamic, closely-knit research team building useful robots and general-purpose robot foundation models.

Implement, extend, and create state-of-the-art methods for robot behavior learning from a mixture of interactive embodied data and online data sources.

Design and implement high-performance machine-learning pipelines and optimize data and learning stacks for scalability, efficiency, and performance.

Be a key member of the team and play a critical role in rapid progress measured by both the development of internal capabilities and high-impact external publication.

Collaborate with internal research scientists and our partner labs at top academic research universities including MIT, Stanford, Berkeley, CMU, Columbia, and Princeton to drive pioneering research at scale.

Qualifications

PhD in computer science, machine learning, robotics, or a closely related field.

Experience training large models and deploying them on embodied systems, particularly toward robotic manipulation.

Strong software development skills in Python, familiarity with mixed C++/Python codebases, and a focus on clean, maintainable code.

Extensive practical experience with Machine Learning using a major framework such as PyTorch or TensorFlow. Familiarity with data pipelines, model serving and optimization, cloud training, and dataset management.

Strong understanding of the state-of-the-art in robot learning, including generative models (e.g., diffusion policy, flow matching), reinforcement learning, and/or world models.

Practical experience with robots and the system integration challenges inherent in conducting research and deploying onto physical hardware platforms.

An ability to move fast and switch between modes of rapid prototyping and robust implementation as required.

A strong track record of impact, either via first author research publications at top-tier machine learning or robotics conferences (RSS, NeurIPS, ICML, CoRL, ICRA, IROS, …), or via meaningful contributions to successful industry initiatives.

Bonus Qualifications

Experience in robotics and machine learning research or related projects in an industry setting.

Experience with robotic middleware such as ROS 2 and common communication methods and protocols.

Experience with modern ML infrastructure pipelines, approaches, and tools.

Experience with VR-based teleoperation for real-time robot control.

Background or familiarity with some of the following: motion control and actuation, whole-body control, reinforcement learning, robot teleoperation methods, common communication protocols, research robotic arms/systems, visual perception and depth sensors, machine learning, robotic simulation, force and tactile sensing systems, haptic interfaces.