Weidan Kong
Staff Engineer at Alibaba.com
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolWeidan Kong is a Staff Engineer with seven years focused on AutoML backend and service platforms, currently leading HyperParameter Optimization and Auto Feature Engineering efforts at Alibaba.com. With prior experience building large-scale monetization and data pipelines for Xiaomi (50M+ DAU) and search relevance systems at Microsoft Bing, she blends product-driven engineering with deep ML system know-how. She has a Master’s in Pattern Recognition and practical expertise integrating cloud-specific compute platforms—demonstrated by contributions that enabled Alibaba’s DLC support in the popular Microsoft NNI AutoML toolkit. Comfortable operating at the intersection of infrastructure, experiment management, and user-facing APIs, she helps teams onboard both internal and public cloud environments. Known for pragmatic problem solving, she often surfaces subtle platform compatibility fixes that unlock real-world ML workflows.
7 years of coding experience
9 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Nanjing UniversityMaster, Pattern Recognition, Master, Pattern Recognition at Institute of Automation, Chinese Academy of Sciences
Github Skills (14)
pytorch10
automated-machine-learning10
automl10
python10
distribute9
deeplearning-ai9
machine-learning9
deep-learning9
hyperparameter-optimization8
tensorflow8
kubernetes-pods7
cloud-services7
kubernetes7
cloud-infrastructure7
Programming languages (2)
Github contributions (5)
microsoft/nni
Aug 2021 - Aug 2022
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Role in this project:
ML Engineer
ML EngineerContributions:25 reviews, 6 commits, 7 PRs in 11 months
Contributions summary:Weidan contributed significantly to the integration and support of Alibaba's DLC (Deep Learning Computing) platform within the NNI framework. Their work involved the creation of a DLC client, modifications to the DLC utility script, and adjustments to the environment service to facilitate the submission, monitoring, and management of trials on the DLC platform. These changes enabled the use of NAS (Network Attached Storage) and OSS (Object Storage Service) data sources within the DLC environment, enhancing its flexibility and compatibility. The user also addressed a bug in the trial dispatcher, improved the experiment listing function, and made updates to the PAI-DLC API.
pythonneural-architecture-searchengineeringtensorflowbayesian-optimization
weidankong/nni
Jul 2021 - Jul 2022
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Contributions:40 pushes, 11 branches in 11 months
pythonneural-architecture-searchengineeringtensorflowhyper
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