Senior Research Engineer, Mechanical Intuition in Multimodal Models

• 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.