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We’re looking for exceptional research interns who are passionate about AI, graphics, and building world-class products together with us. 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.
Job Responsibility:
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
Requirements:
Able to commit to a full-time internship for 12 weeks or longer
Intend to join Meshy full-time after graduation (ideally graduating from 9/2026-9/2027, but later is also acceptable)
Open to undergraduate, master's, or PhD students
Strong in technical fundamentals, with a spirit of innovation and willingness to challenge yourself
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 have:
Familiar with computer graphics, including rendering, geometry processing
Familiar with high performance training on large scale infrastructure (e.g., SLURM, Ray, k8s)
Contributions to popular open-source machine learning projects or publications in top-tier CV / ML conferences
First-author publications in top-tier conferences such as SIGGRAPH, ICLR, etc.
Hands-on experience in multimodal AI or computer graphics, either through research or engineering work