Machine Learning Research Scientist, Mechanical Intuition in Multimodal Models

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 Future Factory team in TRI's Energy and Materials division focuses on developing cutting-edge tools and methods to accelerate change and increase flexibility and efficiency in Toyota's product design and manufacturing, to speed the transition to an emissions-free world. To achieve this we are building end-to-end AI systems that can reason about how physical objects are made — from design intent through to the assembly of real parts — and developing the learning infrastructure needed to train and evaluate these systems at scale.

We are looking for a Research Scientist to join us in building intelligent systems for physical assembly. This role is well-suited for a recent PhD graduate with a strong implementation track record and a genuine curiosity about how things are made.

As a researcher on the team, you will design and implement learning pipelines from scratch, run experiments to evaluate a wide range of architectural, data, and algorithmic choices, and help shape how we apply modern machine learning to the challenges of robotic assembly. You will work at the intersection of policy learning, reinforcement learning, and physical reasoning — and have the opportunity to explore how large language models and agentic infrastructure can be brought to bear on real-world manufacturing problems.