Xuan Qi
Software Engineer at Facebook
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolXuan Qi is a software engineer with 11 years of experience based in Sunnyvale, California, currently working at Facebook. He blends systems and game-development roots with applied machine learning engineering—contributions to PyTorch around dynamic shapes and AOT export show a focus on making complex ML models more portable and robust. Earlier work spans backend infrastructure, authentication systems, and performance optimizations for cross-platform games where he boosted simulation performance by about 200%. He holds a BS in Computational Mathematics and an MS in Computer Engineering, reflecting strong mathematical foundations driving pragmatic engineering. Comfortable across low-level systems, server-side tooling, and ML frameworks, he brings a rare combination of production-scale impact and open-source influence.
11 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computational Mathematics, GPA: 3.6/4, Bachelor's degree, Computational Mathematics, GPA: 3.6/4 at Chongqing UniversityMaster's degree, Computer Engineering, GPA: 3.8/4, Master's degree, Computer Engineering, GPA: 3.8/4 at University of Florida
English, Chinese
Github Skills (10)
pytorch10
machine-learning10
python10
autograd9
deeplearning-ai9
deep-learning9
functional-programming9
tensor9
neural-network8
numpy8
Programming languages (1)
Github contributions (3)
pytorch/pytorch
Jun 2023 - Jul 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:38 reviews, 21 PRs, 199 pushes in 1 month
Contributions summary:Xuan contributed to the PyTorch library, focusing on features related to dynamic shapes and AOT (Ahead-Of-Time) export capabilities. Their work included deprecating and refactoring functionalization flags, creating and integrating new Aten native operations for constraint ranges, and functionalizing assertion operations. This work demonstrates a focus on improving the export process, adding more robust dynamic shape support, and enhancing the overall PyTorch ecosystem for more advanced use cases.
pythongpu-accelerationdeep-learninggpunumpy
XuanQi/TDD-Django
Jan 2015 - Feb 2015
Contributions:12 pushes, 2 branches, 3 tags in 23 days
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