What are the responsibilities and job description for the Machine Learning Research Scientist, Mechanical Intuition in Multimodal Models position at Toyota Research Institute?
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.
\n- 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.
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