Design, train, and refine large-scale 3D generative models from covering pre-training, post-training, and emerging paradigms in diffusion, flow matching, and multi-modal learning.
Bridge the gap between cutting-edge research and product, deploy models in real products used by millions of creators, using human feedback and creative evaluation.
Create novel model architectures to make 3D generation faster, higher-quality, and more controllable.
Collaborate with infrastructure and systems teams to build scalable training, and data pipelines across GPU clusters and cloud environments.
Bring engineering discipline into an fast-paced research environment: elegant code, reproducible experiments, and building software as a team.
Share insights and breakthroughs through internal demos, open-source contributions, or technical reports that advance the field of 3D generative AI.

Strong engineering skills in Python and deep learning frameworks (preferably PyTorch); comfortable moving between research prototypes and production systems.
Familiar with Transformers and modern generative AI models (Diffusion / flow matching, VAE, etc.).
Curiosity and passion for multi-modal AI, and have an intuitive understanding of how models perceive, represent, and generate 3D worlds.
Familiar with high performance training on large scale infrastructure (e.g., SLURM, Ray, k8s) is a plus.
Contributions to popular open-source machine learning projects or publications in top-tier CV / ML conferences is a plus.

Generative AI, 3D Foundation Model

Key skills