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 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.
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.
The pay range for this position at commencement of employment is expected to be between $176,000 and $253,000/year for California-based roles, and between $158,400 and $227,700/year for Massachusetts-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. 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. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.
