Research Scientist – Geometry (AI Assisted) at Stability AI

Research Scientist Geometry (AI Assisted)Location: RemoteAbout the RoleWere 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. Youll 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 Youll DoResearch 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 BringPhD (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 / PreferredResearch 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. Key Skills Laboratory Experience,Machine Learning,Python,AI,Bioinformatics,C/C++,R,Biochemistry,Research Experience,Natural Language Processing,Deep Learning,Molecular Biology Employment Type : Full Time Experience: years Vacancy: 1