Meshy LLC - Generative AI

Sunnycale, United States - Hybrid5mo ago

In Office NA Artificial Intelligence Manufacturing AI Engineer Python Lead Generation Linear Ruby Transformers

Requirements

• You are a 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. You have deep understanding of the transformer architecture, have strong python and tensor programming skills, have a vision for AI beyond linear sequences, and you believe in "the scaling law". You can translate high-level goals into concrete research and implementation steps, set an approach, and follow through. When it's time to explain your ideas, you bring clarity to complex technical issues. You are not afraid of confronting new ideas, and you are eager to share your knowledge with the team. You use these skills to create real-world benefits for our researchers, engineers, and millions of users, and you are excited to help advance our effort to push the state of the art of AI that understands and generates 3D worlds. • 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.

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 by deploying 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.