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Update： base 可在北京，上海，深圳（prefer）， BayArea。 ---- About the Role We are looking for Machine Learning Systems Engineers who can help us build the world's largest end-to-end 3D native machine learning systems. You will help us build our end to end ML framework dedicated for 3D, from pretraining, to finetuning, inferencing, etc. We expect a combination of strong hands on engineering skills, eagerness to learn new things, and thrives in a fast-paced, high-ownership environment. What You’ll Do: Work within the AI model team to streamline 3D data into high-throughput pipelines and scale training infrastructure to hundreds of GPUs. Train, accelerate, and deploy machine learning models for 3D GenAI. Design and implement reliable and scalable distributed training pipelines, optimize end-to-end training efficiency. Work closely with researchers, software engineers, and artists to integrate AI models into production. On the training side Work closely with researchers to build the 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. Building and maintaining training clusters and job schedulers. Implementing and maintaining 3D specific custom operators in Triton or CUDA On the inference side Building efficient inference endpoints with complex model pipelines Optimizing models through compilation, fusion, quantization, etc. What We're looking for: Experience in machine learning or high performance graphics. Solid practical understanding of at least one machine learning framework (e.g. PyTorch, Flax). Strong ability to write beautiful and maintainable code in Python and/or C++. Ability to learn fast and dive into new concepts or complex codebases. Performance and efficiency oriented mindset, with a strong interest in the tiniest detail. Strong communication skills for working in a globally distributed team. Nice to have: A strong passion to navigate through the PyTorch internals, with hands-on experience in areas like torch.compile , fully_shard (FSDP2) APIs. Experience with building Triton kernels. Experiences with large-scale distributed training, familiarity with modern parallelization techniques: DP, TP, CP, PP, zero redundancy optimizers, etc. Experience with diffusion models in 3D or video. Experience with full bf16 or partially fp8 training. If you are interested, please send resume to email, linggaoyuan(at)google(dot)com

坐标深圳，World's No1 3D Generate AI公司，meshy.ai招聘MLE