CAD Automation Researcher

UniversalAGI is building foundation models that learn physics directly from data to transform engineering simulation. The CAD Automation Researcher will develop AI methods for CAD geometry repair, simplification, generation, reconstruction, and design optimization, owning workflows from synthetic data generation through production deployment and collaborating with the founding and physics research teams.

Responsibilities:

• Develop methods to automatically repair, defeature, and simplify raw CAD geometry into simulation ready form, removing today's largest manual bottleneck in our design pipeline
• 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 your 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 our physics research group to make sure geometry and optimization models integrate cleanly with the rest of 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