Generative AI - 3D Foundation Model

Meshy LLC•Sunnyvale, CA

2d•$175,000 - $300,000•Onsite

About The Position

As a core member of the team of research scientists and machine learning engineers at Meshy, you will drive the development of our core 3D-native generative foundational model. In this role, you will join our foundational research to advance 3D AI, apply learnings from other fields of ML, and pushing the state of the art. You will also work towards long-term ambitious research goals, while identifying intermediate milestones.

Requirements

Talented, hands-on researcher who thrives in a fast-paced environment, is self-directed, a team player, and knows how to get things done efficiently.
Deep understanding of the transformer architecture.
Strong python and tensor programming skills.
Vision for AI beyond linear sequences, and belief in "the scaling law".
Ability to translate high-level goals into concrete research and implementation steps, set an approach, and follow through.
Ability to explain complex technical issues with clarity.
Not afraid of confronting new ideas, and eager to share knowledge with the team.
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.

Nice To Haves

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.

Responsibilities

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.