Qi Jing
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolQi Jing is a machine learning engineer with eight years of hands-on experience building and debugging core ML infrastructure, currently based in Seattle. Trained at Southeast University and pursuing graduate studies at UIUC, Qi has contributed substantive backend and operator-level improvements to the widely used PaddlePaddle framework—implementing bmm, adding XPU support, and fixing segmentation faults to improve stability and performance. Their work spans federated learning (scheduler and FEMNIST demo) to distributed training coordination, reflecting both systems-level rigor and applied ML expertise. Past internships at Baidu, DEKA R&D, and AWS complement research experience at Waterloo, indicating a track record of shipping production-ready features and clear technical documentation. Notably, Qi combines low-level debugging aptitude with a developer-focused sensibility for API refactoring and demo-driven adoption.
8 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Artificial Intelligence, Bachelor's degree, Artificial Intelligence at Southeast UniversityMaster of Engineering - MEng, Electrical and Computer Engineering, Master of Engineering - MEng, Electrical and Computer Engineering at University of Illinois Urbana-Champaign
Github Skills (14)
paddlepaddle10
machine-learning10
deeplearning-ai10
deep-learning10
federated-learning10
python10
xpu9
neural-network9
zeromq9
distributed-training8
efficientnet8
cprogramming-language7
c-language7
tensorflow4
Programming languages (4)
Github contributions (5)
PaddlePaddle/PaddleFL
Nov 2019 - Mar 2022
Federated Deep Learning in PaddlePaddle
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 42 reviews, 250 commits in 2 years 4 months
Contributions summary:Qi contributed to the development of a federated deep learning framework by implementing a scheduler for coordinating distributed training and updating the demo examples to utilize the new scheduler functionality. They added a new demo based on the FEMNIST dataset, including the necessary trainer, and demonstrated its execution. In addition to code changes, the user also updated the documentation, ensuring the framework's usability.
meta-learningdeep-learningpaddlepaddlefederated-deep-learningmachine-learning
PaddlePaddle/Paddle
Jan 2020 - Oct 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 & ML Engineer
Back-end Developer & ML EngineerContributions:2 reviews, 13 commits, 63 PRs in 9 months
Contributions summary:Qi's contributions primarily revolve around improving the `paddlepaddle/paddle` deep learning framework. They focused on debugging and enhancing core functionality, including adding gradient checks to reduce operations and fixing segmentation fault bugs within reduce ops. The user also implemented new features, such as the bmm op, which is a fundamental matrix operation and API refactoring to enhance the usability of the framework. Additionally, the user added support for XPU (Kunlun) operators.
pytorchpythonparalleldeep-learningpaddlepaddle
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