Su Zhu
Software Development Engineer II at Amazon Web Services
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSu Zhu is a pragmatic Software Development Engineer II with 11 years of experience building production software at scale, currently driving services at Amazon Web Services from San Jose. Comfortable across backend and frontend stacks, Su has deep Java, JavaScript, PHP, Python and SQL skills and hands-on experience with Ember.js and Angular.js for polished user experiences. Strong foundations in data structures and algorithms inform a disciplined approach to designing robust, performant systems. Beyond cloud services work, Su contributes to the AutoGluon open-source project, improving Kaggle-ready ML examples, data preprocessing pipelines, and semantic search tutorials—bringing practical ML/DS workflows into production. A fast learner and self-starter, Su pairs engineering rigor with a knack for turning complex data pipelines into reproducible submission workflows. Educated at Santa Clara University and Nanjing University of Aeronautics and Astronautics, Su blends formal training with broad, applied software and ML experience.
11 years of coding experience
Bachelor's degree, Bachelor's degree at Nanjing University of Aeronautics and AstronauticsMaster's Degree, Master's Degree at Santa Clara University
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (18)
kaggle10
python10
data-science10
machine-learning10
datatable10
ensemble-learning10
datatables10
deeplearning-ai10
deep-learning10
tabular10
automated-machine-learning10
pytorch9
scikit9
scikit-learn9
natural-language-processing8
Programming languages (4)
Github contributions (5)
autogluon/autogluon
Sep 2022 - Jan 2023
Fast and Accurate ML in 3 Lines of Code
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:1 release, 232 reviews, 13 commits in 4 months
Contributions summary:Su primarily contributed to the AutoGluon project by implementing and improving machine learning examples, particularly for Kaggle competitions. This includes creating and refining data preprocessing pipelines, developing training scripts, and establishing submission workflows. The user also integrated text normalization techniques and added a semantic search tutorial, demonstrating a focus on enhancing the model's capabilities and user guidance.
forecastingimage-textmlppythonmeta-learning
conda-forge/autogluon.core-feedstock
Nov 2023 - Dec 2024
A conda-smithy repository for autogluon.core.
Contributions:1 review, 41 PRs, 23 pushes in 1 year
starcraftcondasmithyautodeskautogluon
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