🕒 25 days ago
Develop reliable model evaluations for research signals, focusing on evaluation creation, usability, auditing, and efficiency in collaboration with researchers and engineers.
Machine Learning Research Scientist, Mechanical Intuition in Multimodal Models
📅 Apr 2
Machine LearningReinforcement LearningPolicy LearningRobotics
📜 Description
- Design and implement end-to-end modeling pipelines for machine assembly tasks.
- Run systematic experiments to evaluate architectural variants and learning techniques.
- Develop and maintain rigorous evaluation protocols for policy performance.
- Explore integration of modern LLMs and agentic systems for physical reasoning.
- Collaborate with researchers and engineers to connect learning systems with hardware.
🛠️ Requirements
- 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.
✨ Benefits
- Medical insurance
- Dental insurance
- Vision insurance
- 401(k) eligibility
- Vacation
- Sick time
- Parental leave
- Annual cash bonus structure
Full job description
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 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.
Responsibilities
Qualifications
Bonus Qualifications
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