Xueke Chen
Product Development Engineer at Intel Corporation
Chengdu, Sichuan, China
Summary
🤩
Rockstar🎓
Top SchoolXueke Chen is a Product Development Engineer with eight years of cross-disciplinary experience driving new product introduction, yield improvement, and production validation across leading semiconductor and technology firms. He has held engineering and leadership roles at Intel, Infineon, Micron, and ON Semiconductor, combining hands-on test program development, big-data product health monitoring, and project management for HVM transfers. Xueke’s work blends hardware-focused process and quality engineering with software-savvy contributions to ML infrastructure—he’s an active contributor to the PlaidML project, adding critical operations like ROIPooling and CumSum to broaden deep-learning compatibility. Known for pragmatic problem solving, he consistently reduces cost and improves yield through cross-functional coordination with fabs, FA teams, and vendors. Based in Chengdu, he pairs a bachelor’s in electric information engineering with a no-nonsense engineering ethos captured in his GitHub motto: “Stop delivering shit, Do some excellent job.”
8 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, electric information engineering, Bachelor’s Degree, electric information engineering at China University of Mining and Technology
English
Github Skills (8)
machine-learning10
deep-learning10
tensorflow10
plaidml10
python10
c-language8
cprogramming-language8
openvino7
Programming languages (3)
Github contributions (5)
plaidml/plaidml
Dec 2020 - Feb 2021
PlaidML is a framework for making deep learning work everywhere.
Role in this project:
ML Engineer
ML EngineerContributions:7 reviews, 5 commits, 7 PRs in 2 months
Contributions summary:Xueke primarily contributed to the implementation of new operations and functionalities within the PlaidML framework, specifically focusing on deep learning tasks. Their work involved adding support for new operations like ReverseSequence, CumSum, and ROIPooling, which are crucial for various neural network architectures. They also made modifications to existing code, including tests, and refactored code. This indicates a focus on extending PlaidML's capabilities and ensuring its compatibility with different deep learning models.
pytorchtvmdeep-learningmachine-learningcompiler
XingHongChenIntel/snake_by_c
Mar 2020 - Jul 2022
a little game for linux
Contributions:2 PRs, 18 pushes, 2 branches in 2 years 3 months
linuxgamec
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