Senior Research Scientist, Robotics Research

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.

The Vision We envision a future with robots that help people in our real environments with interactions that allow robots to understand, adapt, and grow with us. We believe that robots should and will work well alongside people: helping where wanted, and ultimately enabling people to spend more time on the activities they enjoy most. To achieve this, robots need to be able to operate reliably in messy, unstructured environments. We must also create robots that people can understand, collaborate with, and rely on. The Team Our goal is to revolutionize the field of robotics, enabling long-horizon dexterous behaviors to be efficiently taught, learned, and improved over time in diverse, real-world environments with people.Our team has deep cross-functional expertise across hardware, simulation, perception, controls, and machine learning. We measure our success in terms of fundamental capabilities development, as well as research impact via open-source software and publications. Come join us and let’s make general-purpose robots a reality.

Some of our ongoing work is highlighted here. The Opportunity We’re looking for a driven researcher with a “make it happen” mentality. The ideal candidate is able to operate independently when needed, but works well as part of a larger integrated group at the cutting edge of state-of-the-art robotics and machine learning. If our mission of revolutionizing robotics through machine learning resonates with you, get in touch and let’s talk about how we can create the next generation of AI-powered capable robots together!

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.

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