Senior Research Scientist, Large Behavior Models

• 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.

• 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. • 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.