Research and deploy generalist robot-learning policies for Asimov, taking ideas from datasets and training through evaluation on physical hardware, while building systems that help the robot learn from its own experience.
Research Scientist, Robot Learning
📜 Description
- Advance robot learning models using language, video, action, and multimodal data.
- Implement and develop robot behavior learning methods, including reinforcement and imitation learning.
- Train large models and deploy them on embodied systems, particularly for robotic manipulation.
- Collaborate with research scientists, engineers, and academic partner labs.
- Contribute to internal capabilities and high-impact research publications.
🛠️ Requirements
- 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.
✨ Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- 401(k) eligibility
- Paid vacation
- Paid sick time
- Parental leave
- Annual cash bonus structure
Full job description
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.
Responsibilities
- Work as part of a dynamic, closely-knit research team pushing the bounds of what robots can do.
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Study and advance the science of robot learning models, including language, video, action, and multimodal data.
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Implement, extend, and create state-of-the-art methods for robot behavior learning, drawing from techniques such as reinforcement learning, imitation learning, and related methods.
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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.
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Collaborate with internal research scientists, engineers, 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.
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Experience with robotic middleware such as ROS 2 and common communication methods and protocols.
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Experience with modern ML infrastructure pipelines, approaches, and tools.
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Experience with VR-based teleoperation for real-time robot control.
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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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