Shixin Li
Software Engineer at Google
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolShixin Li is a software engineer with nine years of experience specializing in mobile robot perception, visual-inertial tracking, and ML infrastructure. Based in Mountain View, he has shipped ARCore tracking algorithms and contributed 3D terrain reconstruction for Alphabet's Wing while driving TensorFlow AOT compilation and MLOps improvements at Google. His background spans robotics research (UCSD, CMU) and industry internships where he built SLAM systems, sensor calibration tools, and deep-learning-based stereo and segmentation pipelines. A strong open-source contributor, he has committed performance and serialization enhancements to the flagship tensorflow/tensorflow repository, bridging research-grade perception work with production ML tooling. Known for combining rigorous academic training (MS in Robotics, UCSD) with pragmatic engineering, he quietly excels at turning complex perception algorithms into deployable systems.
9 years of coding experience
2 years of employment as a software developer
B.E., Electrical Engineering, 3.8, B.E., Electrical Engineering, 3.8 at Sichuan UniversityUniversity of California, San Diego
Chinese, Chinese
Github Skills (12)
compiler10
machine-learning10
deep-learning10
tensorflow10
python10
aot10
compile10
buildr9
mlops9
c-language8
distributed-systems8
cprogramming-language8
Programming languages (4)
Github contributions (5)
tensorflow/tensorflow
Nov 2022 - Jan 2023
An Open Source Machine Learning Framework for Everyone
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:3 commits in 1 month
Contributions summary:Shixin's commits primarily focus on modifying and optimizing functions related to the TensorFlow framework, particularly around AOT (Ahead of Time) compilation. They refactored code for function optimization, including the integration of optimized function graphs into the library and the addition of metrics for tracking optimization performance. Additionally, the user contributed to the build process and serialization of executables, suggesting involvement in MLOps tasks such as model deployment and management. Their contributions touch upon core components of TensorFlow and enhance compilation and performance.
pythondata-sciencedeep-learningmlmachine-learning
MaidouPP/.emacs.d
Jul 2017 - Oct 2020
Contributions:67 pushes, 2 branches, 9 comments in 3 years 3 months
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