Research Scientist – Geometry (AI Assisted)
Location: Remote
About the Role
We’re looking for a Research Scientist with a strong foundation in geometry processing and a deep interest in how modern learning systems can represent and reconstruct shape. You’ll work at the intersection of discrete and differential geometry, shape tokenization, and generative modelling, developing methods for unwrapping, remeshing, and reconstructing 3D geometry that are compact, controllable, and scalable.
What You’ll Do
- Research and develop AI-assisted geometry processing pipelines for UV unwrapping, remeshing, geometric reconstruction, and shape generation.
- Design learning-based representations for geometry (meshes, point clouds, implicit fields) that capture structure, topology, and correspondence.
- Develop token- or patch-based encoders for shape representation, enabling compression, editing, and reconstruction from learned codes.
- Integrate learned geometry modules into generation and reconstruction frameworks, ensuring geometric validity and multi-view consistency.
- Build training and evaluation pipelines with quantitative metrics for distortion, reconstruction fidelity, and mesh topology quality.
- Collaborate with graphics, simulation, and ML teams to bring new geometry models into creative and production pipelines.
- Contribute to publications, benchmarks, and internal best practices in geometry + AI research.
What You Bring
- PhD (or equivalent experience) in Computer Graphics, Geometry Processing, Machine Learning, or a related field.
- Deep understanding of discrete and differential geometry, including remeshing, surface parameterization, and geometric optimization.
- Experience with learning-based geometry representations (e.g., geometric autoencoders, tokenization, learned unwrapping, or generative reconstruction).
- Strong engineering skills: proficiency with PyTorch/JAX, geometry/mesh libraries, and large-scale experiment pipelines.
- Ability to bridge classical geometry processing with modern learning-based techniques and apply them to practical 3D workflows.
Bonus / Preferred
- Research or open-source work in learning-based UV unwrapping, remeshing, or geometry reconstruction.
- Experience developing generative 3D models or integrating learned geometry modules into diffusion / flow-matching frameworks.
- Familiarity with implicit neural representations, differentiable rendering, or geometry-aware latent spaces.
- Experience integrating geometry systems into 3D toolchains (e.g., Blender, Unreal, Unity) or graphics pipelines.
Equal Employment Opportunity:
We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.
