Yifan Jiang
Senior Staff Software Engineer at NVIDIA
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolYifan Jiang is a Senior Staff Software Engineer and LLM systems leader with a decade of experience building scalable ML infrastructure and inference platforms for Google, ByteDance, and now NVIDIA in the San Francisco Bay Area. He has driven end-to-end LLM serving, fine-tuning, RL, and multi-host GPU inference work—collaborating closely with compiler teams (XLA) and cloud teams to make large models performant across hardware and frameworks. His open-source contributions to TensorFlow include XLA memory and compilation optimizations and fixes that improved GPU computation reliability, reflecting a rare blend of deep systems engineering and ML research experience. Earlier academic work in 3D perception and crowd-sourced dataset collection shows a practical research mindset that complements his production-first approach to MLops and serving.
10 years of coding experience
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at University of MichiganBachelor's degree Electrical and Computer Engineering, Bachelor's degree Electrical and Computer Engineering at Shanghai Jiao Tong University
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
xla10
machine-learning10
tensorflow10
gpu10
python10
deeplearning-ai9
deep-learning9
c-language8
mlops8
cprogramming-language8
deep-neural-networks7
distribute7
neural-network7
Programming languages (1)
Github contributions (5)
tensorflow/tensorflow
May 2022 - Dec 2022
An Open Source Machine Learning Framework for Everyone
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:14 commits in 7 months
Contributions summary:Yifan's contributions primarily involve optimizing and refining the TensorFlow framework's internal workings. They modified XLA compiler code to reduce memory usage and improve compilation efficiency within the XLA runtime environment. Additionally, the user addressed issues related to SavedModel loading, ensuring that the function library is properly updated for lazy loading and other improvements to XLA launch operations. Moreover, the user focused on improving the efficiency and reliability of GPU-based computations.
pythondata-sciencedeep-learningmlmachine-learning
yifjiang/relative-depth-using-pytorch
May 2017 - Apr 2018
Contributions:65 commits, 11 pushes, 1 branch in 11 months
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