CAD Automation Researcher
San FranciscoFull-timePosted 3w ago
About the role
Role focused on building AI for physics simulation by automating CAD geometry repair, defeature, and optimization processes; work directly with CEO and founding team to integrate geometry and optimization models into UniversalAGI's foundation model stack.
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.
Qualifications
- 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: 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, such as 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 rather than only fine tune existing ones.
- High execution velocity and comfort operating with significant ownership in a fast moving startup environment.
Skills
Required:CAD geometry processingGeometry-aware deep learningOptimization for physical designPython & DL frameworks
Benefits
- Competitive compensation and equity.
- Competitive health, dental, vision benefits paid by the company.
- 401(k) plan offering.
- Flexible vacation.
- Team Building & Fun Activities.
- Great scope, ownership and impact.
- AI tools stipend.
- Monthly commute stipend.
- Monthly wellness / fitness stipend.
- Daily office lunch & dinner covered by the company.
- Immigration support.
Last updated Oct 14, 2026. Interested? Apply on the company site.
About UniversalAGI
Foundation models for automating physical systems engineering
The platform for engineering teams building and operating Physics AI at scale. Predict aerodynamic performance, structural loads, and thermal behavior in seconds.
PlatformAI simulationUncertainty QuantificationLIFT Volume TokenizationCalibrated UncertaintySUV-PTAerospaceDefense
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