Baojun Liu
Software Engineer at Microsoft
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolBaojun Liu is a seasoned software engineer with eight years of experience building high-performance deep learning systems and compiler/runtime infrastructure, currently working on Michelangelo at Uber and onboarding NLP models at Microsoft. He has deep expertise in C++, Python, and ML frameworks (TensorFlow, PyTorch, ONNX, PaddlePaddle) and a track record designing and optimizing Intel’s nGraph compiler for heterogeneous accelerators. Baojun combines backend engineering, DevOps, and performance tuning—evidenced by contributions that enabled nGraph integration in PaddlePaddle and release/build improvements for Intel Nervana’s neon framework. He holds a Ph.D. in Electrical and Computer Engineering and leverages that research background to bridge algorithmic needs with production-grade tooling. Based in the San Francisco Bay Area, he’s comfortable refactoring core libraries, adding fused operators, and shipping defaults that improve usability and efficiency at scale.
8 years of coding experience
9 years of employment as a software developer
B.Sc, B.Sc at University of Science and Technology of ChinaDoctor of Philosophy (Ph.D.), Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering at University of Pittsburgh
English, Chinese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (22)
c-language10
python10
back-end-development10
operation10
tensorrt10
machine-learning10
deep-q-learning10
deep-learning10
tensorflow10
performance-optimization10
paddlepaddle10
tensor10
cprogramming-language10
distributed-training9
build-system9
Programming languages (3)
Github contributions (5)
PaddlePaddle/Paddle
Nov 2018 - Jan 2020
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:64 commits, 68 PRs, 1 push in 1 year 2 months
Contributions summary:Baojun primarily worked on integrating the nGraph engine within the PaddlePaddle framework. Their contributions involved setting up and configuring the build environment to utilize nGraph, ensuring it was enabled by default. They also implemented and modified core components to support nGraph, including adding fused operators and integrating various functionalities. The user was responsible for refactoring and restructuring the code to improve efficiency.
pytorchpythonparalleldeep-learningpaddlepaddle
NervanaSystems/ngraph
Aug 2019 - Mar 2020
nGraph - open source C++ library, compiler and runtime for Deep Learning
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:42 commits, 31 PRs, 202 pushes in 6 months
Contributions summary:Baojun primarily focused on enhancing the nGraph library by adding new functionalities related to automatic broadcasting, specifically for PaddlePaddle (PDPD) style broadcasting, and making the softmax axes dynamic. They also made significant contributions to the implementation of a partial slice fused operation. Furthermore, they implemented various improvements and refactoring across multiple files within the library.
inference-enginecppc-librarydeep-learningtvm
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