Leo Jiang
Principal Software Engineer at 甲骨文
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolLeo Jiang is a Principal Software Engineer based in Beijing with over a decade of experience spanning Linux systems (C/C++), Java back-end development, and NLP research and services. He has led core engineering work at Adobe—designing and integrating machine translation into localization platforms—and now drives architecture and delivery at Oracle, focusing on distributed systems, cloud services, and I18n/L10n. Equally at home in low-level system optimization and higher-level web services, he also contributes to ML infrastructure upstream, including performance adaptations of Hugging Face’s diffusers for Huawei NPU. A pragmatic agile advocate and lifelong learner, he blends research-rooted expertise (Master’s-level systems and translation work) with hands-on engineering to ship production-ready solutions that boost developer and business productivity.
5 years of coding experience
9 years of employment as a software developer
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at Nankai UniversityMaster's degree, Computer Science, Master's degree, Computer Science at Institute of Software Chinese Academy of Sciences
English
Github Skills (7)
memory-management10
pytorch10
deeplearning-ai10
deep-learning10
performance-optimization10
stable-diffusion9
image-generation9
Programming languages (1)
Github contributions (5)
huggingface/diffusers
Aug 2024 - Feb 2025
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
Role in this project:
ML Engineer
ML EngineerContributions:15 reviews, 22 PRs, 93 comments in 6 months
Contributions summary:Leo primarily contributed to optimizing the performance and adapting the `diffusers` library for the Huawei NPU (Neural Processing Unit). Their work involved fixing data type errors, addressing memory management issues, and implementing NPU-specific adaptations for Flux, RMSNorm, and the Sana training example. These changes aimed to improve performance and reduce memory consumption, particularly in the context of training and running diffusion models.
pytorchartdeep-learningimage2imagestate-of-the-art
leisuzz/diffusers
Aug 2024 - Feb 2025
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Contributions:106 pushes, 12 branches in 6 months
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