Chunqing Shan
Machine Learning Engineer at Google DeepMind
Zürich Metropolitan Area Switzerland
Summary
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Rockstar🎓
Top SchoolChunqing Shan is a Machine Learning Engineer with 13 years of experience building high-performance AI systems and distributed infrastructure, now contributing to Google DeepMind’s Gemini engine to push models toward roofline-level efficiency. He has a track record of shipping production-grade optimizations—from leading kernel and sharding strategies for inference to improving numpy.save performance as an open-source contributor—combining low-level systems work with pragmatic product thinking. As a former founder and CEO who engineered microservices handling 1k+ RPS and deployed on Kubernetes, he blends startup grit with large-scale research engineering. Based in Zürich, he also led supercomputing efforts at Zhejiang University that set HPL/HPCG records, a sign of deep expertise in performance tuning beyond typical ML roles. Known for meticulous testing and quality work in major projects like NumPy, he brings both operational rigor and a mission to make advanced AI fast, cheap, and accessible.
13 years of coding experience
9 years of employment as a software developer
High School Diploma, High School Diploma at Hangzhou Xuejun High SchoolBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Zhejiang University
English, Chinese, German
Github Skills (5)
pytest10
python10
numpy10
test-automation10
testing10
Programming languages (13)
Github contributions (5)
numpy/numpy
Sep 2022 - Sep 2022
The fundamental package for scientific computing with Python.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:6 reviews, 2 commits, 5 PRs in 1 day
Contributions summary:Chunqing focused on improving the testing infrastructure and ensuring the quality of the NumPy library. Their contributions primarily involved adding new test cases, specifically to address deprecation warnings related to the 'fix_imports' flag in the `numpy.save` function. Additionally, the user made changes to existing tests, including modifications to the testing environment path generation, and updating deprecation target versions. These changes contribute to a more robust and reliable testing process for the NumPy project.
pythonscientific-computingnumpy
vxst/DFA_NFA
Jan 2016 - Jul 2020
Contributions:2 pushes, 1 branch in 4 years 7 months
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