Machine Learning Research Scientist, Mechanical Intuition in Multimodal Models

The Team

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.

The Opportunity

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.

• Design and implement end-to-end modeling pipelines for machine assembly tasks, building from the ground up rather than adapting existing frameworks.
• Run systematic experiments to evaluate architectural variants, data collection and curation strategies, and a range of supervised and reinforcement learning techniques for physical manipulation.
• Develop and maintain rigorous evaluation protocols to measure policy performance across assembly scenarios, including generalization to novel parts, configurations, and failure modes.
• Explore how modern LLMs and agentic systems can be integrated to support physical reasoning and task planning in assembly contexts.
• Collaborate with researchers and engineers across TRI and Toyota's broader ecosystem to connect learning-based systems with real hardware and manufacturing workflows.
• Contribute to writing and publishing research results in peer-reviewed venues.

• A PhD in a relevant field such as Computer Science, Robotics, Mechanical Engineering, or a related discipline, completed recently (or nearing completion), with some post-PhD or internship work experience.
• A demonstrated track record of implementing non-trivial learning systems — not just running baselines, but building pipelines and components from scratch.
• Hands-on experience with policy learning, reinforcement learning, or robot learning, with strong intuitions about what makes these approaches succeed or fail in practice.
• Proficiency in Python and comfort working across the full stack of a research project, from data processing to model training to evaluation.
• Genuine interest in how physical products are designed and manufactured.

• Familiarity with large language models, vision-language models, or agentic AI frameworks, particularly in contexts involving structured reasoning or tool use.
• Experience with robot manipulation, motion planning, or sim-to-real transfer.
• Exposure to manufacturing processes, assembly planning, or CAD/CAM toolchains.
• Experience building or contributing to production-level research codebases.