Senior Research Engineer, 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 designed and made — from geometry and constraints through to simulation and assembly — and developing the engineering infrastructure needed to train, evaluate, and iterate on these systems at scale.

The Opportunity

We are looking for a Senior Research Engineer to join us in building the systems and tools that power our research on physical AI. This role is well-suited for someone with a strong software engineering foundation, deep experience working with geometry or physical modeling, and a genuine interest in how things are made.

As a research engineer on the team, you will design and build the pipelines and tooling that allow researchers to move fast and measure what matters — from large-scale training and evaluation infrastructure to the geometry processing and physics-aware components at the core of our models. You will work at the intersection of software engineering and research, translating emerging ideas into robust, production-quality implementations.

Design, build, and maintain robust and efficient pipelines for model training and evaluation, with a focus on reliability, scalability, and researcher productivity.

Develop tools and frameworks to measure and improve model performance across multiple dimensions, including accuracy, generalization, and computational efficiency.

Collaborate closely with researchers to translate emerging techniques and experimental findings into clean, production-ready implementations.

Build high-performance systems for geometry manipulation, processing, and modeling, including integration with CAD, CAM, or related geometric representations.

Contribute to the team's shared infrastructure and codebase, raising the standard for code quality, testing, and documentation.

An MS or equivalent in Computer Science, Robotics, Mechanical Engineering, or a related field, plus several years of relevant industry or research experience.

A strong track record of designing and shipping reliable software systems, with an ability to work across the full stack of a research engineering project.

Experience working with computational geometry, CAD, CAM, or graphics systems, and a clear understanding of the challenges involved in processing and representing complex 3D geometry.

Experience building performant systems for geometry manipulation or modeling — including efficient data structures, algorithms, or GPU-accelerated pipelines.

Interest in manufacturing, simulation, or process automation, and enthusiasm for working in a domain where software has direct physical consequences.

Familiarity with topology optimization, constraint solving, or CSG representations, and experience applying these in applied research or production contexts.

Experience with physical modeling in some form — finite element analysis, neural ODEs or PDEs, or physical simulation frameworks such as MuJoCo, Taichi, or similar tools.

Exposure to machine learning model development, including training pipelines, evaluation harnesses, and experiment tracking at scale.

Prior experience in a research lab or research-adjacent engineering role, with an appreciation for how to balance rigor and velocity.