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Generative AI - ML System Engineering

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Meshy LLC

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Location:
United States , Sunnyvale

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Category:
IT - Software Development

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Contract Type:
Not provided

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Salary:

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Job Description:

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.

Job Responsibility:

  • 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
  • Building efficient inference endpoints with complex multi-stage model pipelines
  • Optimizing models through compilation, fusion, quantization, etc

Requirements:

  • Experience in machine learning or high performance graphics
  • Solid practical understanding of at least one machine learning framework (e.g. PyTorch, JAX)
  • 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 low precision bf16 or fp8 training

Additional Information:

Job Posted:
February 18, 2026

Employment Type:
Fulltime
Work Type:
Hybrid work
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