Research Scientist, Robot Learning - Continual Adaptation
Toyota Research Institute · Posted Nov 10, 2026
- Cambridge, Massachusetts, United States
- $158K–$228K per year
Lead research on post‑training methods for generative robot policies, enabling rapid adaptation and continual learning in real‑world robotic systems.
Compensation
The pay range for this position at commencement of employment is expected to be between $158,400 and $227,700 /year for Massachusetts-based roles.
Responsibilities
- Design and implement methods for adapting generative robot policies using reinforcement learning, imitation learning, agentic tool use, and related methods.
- Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human‑driven signals.
- Develop rollout‑based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on‑policy rollouts, in both simulation and on real robot hardware.
- Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.
- Publish research outcomes and contribute to the open robotics and embodied AI communities.
Requirements
- Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.
- Nice to have: human‑in‑the‑loop robot learning