CAD Automation Researcher

UniversalAGI is building foundation models that learn physics directly from data, already running in early deployments on computational fluid dynamics and reservoir engineering problems for large industrial and defense organizations. As a CAD Automation Researcher, you will build foundation AI models that remove the biggest bottleneck in engineering simulation: turning messy, real-world CAD into geometry a computer can reason about. You will work directly with the CEO and founding team on the hours lost repairing CAD files, manual de-featuring before simulation, and hand-tuned topology optimization. The role is based in San Francisco, 5 days a week onsite.

Responsibilities

Develop methods to automatically repair, defeature, and simplify raw CAD geometry into simulation-ready form; Develop general-purpose optimization techniques, including topology optimization, shape optimization, and differentiable design search, that operate directly on learned geometry representations and AI physics model outputs; Design and train models, such as implicit neural representations, graph neural networks, and diffusion models, for geometry generation and reconstruction from imperfect, incomplete, or multi-format CAD inputs (STEP, IGES, mesh, point cloud) or embeddings; Own workflow end to end: synthetic geometry data generation, training, evaluation, and deployment into UniversalAGI's production simulation pipeline; Collaborate directly with the CEO, founding team, and physics research group to integrate geometry and optimization models with the foundation model stack; Help define UniversalAGI's technical point of view on AI-driven geometry generation and design optimization, and publish results internally and, where appropriate, externally

Requirements

PhD in Computer Science, Mechanical Engineering, Computational Geometry, or a related field, or equivalent hands-on industry research experience; 2+ years building deep learning models for geometry processing, computer graphics, computational geometry, or 3D generative modeling; Deep, hands-on understanding of CAD representations and geometry processing, including boundary representations (B-rep), CAD kernels such as OpenCASCADE or Parasolid, mesh generation, and defeaturing or healing techniques; Proven research track record applying modern architectures (graph neural networks, implicit neural representations, transformers, or diffusion models) to 3D geometry or physical design problems; Practical experience with optimization methods relevant to physical design, such as topology optimization, shape optimization, or differentiable and gradient-based design search; Expert Python and deep learning framework proficiency (PyTorch or JAX), with the ability to build and train models from scratch; High execution velocity and comfort operating with significant ownership in a fast-moving startup environment

Nice to have

Published research in top-tier venues such as NeurIPS, ICML, ICLR, SIGGRAPH, or CVPR; Experience building on or extending commercial CAD kernels or CAD or CAM software internals; Familiarity with reinforcement learning or large language model based approaches to design or geometry automation; Domain expertise in aerospace, automotive, or industrial manufacturing design workflows; Open source contributions to geometry processing, CAD, or 3D deep learning libraries; Experience at a high growth AI or engineering software startup

Benefits

Competitive compensation and equity; Competitive health, dental, and vision benefits paid by the company; 401(k) plan offering; Flexible vacation; Team building and fun activities; Great scope, ownership and impact; AI tools stipend; Monthly commute stipend; Monthly wellness / fitness stipend; Daily office lunch and dinner covered by the company; Immigration support; Opportunity to shape the technical foundation of a rapidly growing foundational AI company; Direct collaboration with the founder and CEO; Access to world-class investors and advisors in the AI space