Xingyu Liu
Senior Software Engineer at Meta
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolXingyu Liu is a Senior Software Engineer based in Menlo Park with seven years of experience building production systems and ML infrastructure, currently working on LLM inference at Meta. He combines a strong research background—MS in Data Science from Harvard and coursework at MIT—with hands-on engineering roles across major tech firms including Google Summer of Code, Microsoft, TikTok, Tencent, Intel, and Meta. A proven contributor to the flagship SciPy library, he has delivered performance-critical bug fixes, benchmarks, and Pythran-optimized implementations for statistical routines, demonstrating both numerical rigor and performance tuning. Xiaoyu’s cross-disciplinary roots in biomedical engineering and data science underpin a pragmatic approach to algorithms and systems, often surfacing optimizations that bridge research and production. Colleagues rely on him for shipping robust, high-performance components that scale in real-world ML workloads.
7 years of coding experience
4 years of employment as a software developer
Cross-registered, Computer Science, Cross-registered, Computer Science at Massachusetts Institute of TechnologyMaster of Science - MS, Data Science, Master of Science - MS, Data Science at Harvard UniversitySummer School, Engineering, Summer School, Engineering at King's College LondonBachelor of Engineering - BE, Biomedical Engineering, Bachelor of Engineering - BE, Biomedical Engineering at Shanghai Jiao Tong University
Github Skills (8)
statistics10
algorithms10
scipy10
benchmarking10
benchmark10
python10
scientific-computing10
numpy10
Programming languages (6)
Github contributions (5)
scipy/scipy
Mar 2021 - Nov 2022
SciPy library main repository
Role in this project:
Data Scientist / Backend Developer
Data Scientist / Backend DeveloperContributions:25 reviews, 33 commits, 20 PRs in 1 year 8 months
Contributions summary:Xingyu primarily contributed to bug fixes and performance improvements within the SciPy library, specifically addressing issues related to distance calculations, Kendall's tau, and binned statistics. They added benchmarks to evaluate the performance of various statistical functions, including f_oneway, energy_distance, and wasserstein_distance. Furthermore, the user refactored code to use the new random API and utilized Pythran to optimize the performance of the somersd and siegelslopes functions.
scipypythonscientific-computing
charlotte12l/scipy
Mar 2021 - Sep 2021
Scipy library main repository
Contributions:3 PRs, 148 pushes, 41 branches in 5 months
scipypython
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