Research Scientist, Robotics VLAs Post-Training and Adaptation
About The Position
At Toyota Research Institute (TRI), weâre on a mission to improve the quality of human life. Weâre developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, weâve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences. Overview We are seeking a creative and technically strong researcher to advance post-training methods for Vision-Language-Action (VLA) models in robotics. This role focuses on improving model alignment, robustness, and adaptability in real-world robotic settings through advanced post-training and continual learning techniques. You will develop algorithms and frameworks that enable persistent learning and optimize data efficiency in embodied systems.
Requirements
- Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or related field, or equivalent applied research experience.
- Expertise in reinforcement learning, imitation learning, and multimodal representation learning.
- Strong proficiency with deep learning frameworks (e.g., PyTorch, JAX) and robotics simulation environments (e.g., MuJoCo, IsaacSim, PyBullet, Habitat).
- Experience with sim-to-real transfer, policy adaptation, or continual learning in embodied settings.
- Strong coding and experimental skills with an emphasis on reproducibility and evaluation at scale.
- Prior robotics experience with real-world hardware and ML-based robot deployments.
Nice To Haves
- Prior work on VLA models (e.g., PI0/PI0.5, OpenVLA, custom models).
- Experience building or managing robot data collection infrastructure.
- Familiarity with real-world robot platforms (e.g., Franka, Humanoids, or mobile manipulators).
- Publications in top-tier conferences (CoRL, RSS, NeurIPS, ICLR, ICML, ICRA, CVPR).
Responsibilities
- Post-training and adaptation: Design and implement post-training pipelines for VLA models using techniques such as reinforcement learning (RL), reinforcement learning from human or preference feedback (RLHF/RLAIF), in-context learning. Experience with real-world RL is a plus!
- Sim-to-real transfer: Develop methods to enhance real-world transferability of policies trained in simulation.
- Reset-free and continual learning: Explore and implement reset-free and autonomous data collection strategies that enable continual skill improvement without manual resets or supervision. Learn continually under settings with large-scale, long term data collection.
- Structured exploration: Investigate exploration algorithms that balance safety, curiosity, and efficiency for data gathering in both simulation and real-world robotic systems.
- Data curation and feedback loops: Lead the design of data collection and curation pipelines for exploration and post-training, using multimodal data from demonstrations, teleoperation, and on-policy rollouts.
- Collaborate across teams in perception, control, and ML infrastructure to deploy scalable and reproducible research systems.
- Publish research outcomes and contribute to the open robotics and embodied AI communities.
Benefits
- TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree
Number of Employees
101-250 employees
