Tri Source International

Machine Learning Research Scientist, Mechanical Intuition in Multimodal Models

Los Altos, CA; Cambridge, MA · Intern · Internship

Sponsorship not specifiedDetected 111 days ago

PythonFull-Stack DevelopmentMachine LearningLLMsAgentic AIRoboticsCAMMechanical DesignResearch

About the role

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.
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.
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.

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.
Proficiency in Python and comfort working across the full stack of a research project, from data processing to model training to evaluation.
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.
Genuine interest in how physical products are designed and manufactured.
Bonus Qualifications
Familiarity with large language models, vision-language models, or agentic AI frameworks, particularly in contexts involving structured reasoning or tool use.
Experience building or contributing to production-level research codebases.

Experience with robot manipulation, motion planning, or sim-to-real transfer.
Exposure to manufacturing processes, assembly planning, or CAD/CAM toolchains.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment.
An employer who violates this law shall be subject to criminal penalties and civil liability.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.

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.

Collaborate with researchers and engineers across TRI and Toyota's broader ecosystem to connect learning-based systems with real hardware and manufacturing workflows.

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 listing is sourced directly from Tri Source International's careers page and normalized into a canonical job model.