Generative AI - ML System Engineering

About the role

At Meshy, we believe 3D creation should be boundless and accessible. Our mission statement is simple: unleash creativity . We built a full pipeline for 3D content ranging from text / image to 3D, texturing, texture editing, animation rigging, etc. We also built a vibrant community for our creators, where people can share their work, take inspiration from others, and even use it as an asset marketplace for their games and prototypes. We are the market leader in 3D generative AI, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games survey), and we generate real value and is used by enterprises (including Meta, Square Enix, Deepmind, etc.) and millions of end users. Meshy is used in game and film production, in 3D printing, in industrial product design, in enablement of novel product features such as user-generated content, and even in training and simulation for robotics and physical AI. Your next challenge 3D is the brave new frontier of Gen AI. Our work here involves a lot of unique new challenges in both training and inference. Your next challenge at Meshy would involve the full stack of AI, from debugging and monitoring the hardware platform, building training framework, scaling high-throughput 3D data pipelines for our foundational training, co-designing novel model architectures with researchers, to the novel challenge of efficient inference engines for diffusion models and more. Here are some examples for each side of the challenge: On the training side Work closely with researchers to co-design the next frontier of 3D & Spatial AI. Build and debug on top of modern PyTorch, for maximum parallelism and efficiency, and build clean and intuitive training infrastructure for our in-house foundational models. Identifying bottlenecks and optimizing for high throughput & efficient distributed model training across hundreds to thousands of GPUs. Implementing and maintaining 3D specific custom operators in Triton or CUDA. Implementing and maintaining novel data-loading framework and libraries. On the inference side Building efficient inference endpoints with complex multi-stage model pipelines. Optimizing models through compilation, fusion, quantization, etc.