Zhiqiang Wei
Senior Machine Learning Engineer at Baidu, Inc.
Pudong, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolZhiqiang Wei is a Senior Machine Learning Engineer with seven years’ experience building production ML systems and optimizing inference for mobile and edge platforms. He has contributed to core PaddlePaddle projects, implementing X86 kernels and sequence operators that improved model inference and enabling features used across the Paddle ecosystem. His industry experience spans Microsoft (local search and Transformer/BERT models for POI and extraction), China Mobile (time-series throughput prediction and SeqGAN-based protocol fuzzing), and currently Baidu, reflecting a strong blend of applied research and engineering. Comfortable across back-end systems, mobile demos, and technical writing, he pairs model development with pragmatic engineering—often focusing on sequence-model optimizations and deployment-level robustness. A University of Edinburgh AI master’s graduate, he brings both academic rigor and hands-on open-source impact to production ML challenges.
7 years of coding experience
6 years of employment as a software developer
Master's degree Artificial Intelligence, Master's degree Artificial Intelligence at The University of EdinburghBachelor's degree Computer Science, Bachelor's degree Computer Science at Hangzhou Dianzi University
Github Skills (23)
opencv10
c-language10
python10
machine-learning10
deep-q-learning10
math10
maths10
x8610
math-functions10
api-documentation10
deep-learning10
android10
model-optimization10
paddlepaddle10
computer-vision10
Programming languages (3)
Github contributions (5)
PaddlePaddle/Paddle-Lite
Aug 2019 - Jun 2022
PaddlePaddle High Performance Deep Learning Inference Engine for Mobile and Edge (飞桨高性能深度学习端侧推理引擎）
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:588 reviews, 573 commits, 1708 PRs in 2 years 10 months
Contributions summary:Zhiqiang primarily contributed to the X86 backend of the Paddle-Lite deep learning framework. Their work focused on implementing and testing new mathematical functions, including sequence-related operations like sequence scaling and padding, indicating a focus on model optimization and inference. The user implemented new operators (floor, elementwise division, assign), and added kernels for operations like matrix multiplication (LSTM), and specific content-dnn model related functionality such as search_fc and sequence_topk_avg_pooling which suggest contributions in support for the development of machine learning models. These tasks indicate a role encompassing both back-end development and machine learning engineering.
inference-enginemobilebaidutensorflowfpga
PaddlePaddle/Paddle
Oct 2019 - Jul 2022
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
Back-end DeveloperContributions:77 reviews, 32 commits, 91 PRs in 2 years 9 months
Contributions summary:Zhiqiang contributed to the PaddlePaddle framework by modifying and improving existing code, focusing on enhancing documentation and fixing bugs. Their work involved modifications to readers, parameter attributes, and control flow layers, indicating a focus on the framework's core functionalities. Furthermore, the user addressed code style issues and contributed to tests, specifically the compile-time vs. runtime consistency check, suggesting a role in maintaining and improving code quality.
pytorchpythonparalleldeep-learningpaddlepaddle
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