Jerry Wei
Software Engineer at Meta
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolJerry Wei is a software engineer with eight years of hands-on experience building core infrastructure and product-facing systems across top tech companies in the Bay Area, currently on Meta’s Core Growth team. A University of Waterloo Computer Science graduate, he has rotated through high-impact roles at Amazon, X, Census (Fivetran), and startups, blending backend, full-stack, and automation expertise. He has practical experience shipping reverse-ETL and profitability automation features as well as internal tools and platform services that scale. On the research side, he contributed as an ML engineer to a Nature Scientific Reports project implementing a sliding-window deep learning pipeline for histopathology classification, demonstrating applied ML and data-science chops beyond typical backend work. Based in Palo Alto, he balances rapid product delivery with careful model evaluation and hyperparameter tuning, making him comfortable at the intersection of production systems and machine learning.
8 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at University of Waterloo
Github Skills (18)
pytorch10
python10
machine-learning10
resnet10
medical-image-analysis10
computer-vision10
ca10
evaluation9
splits9
trainings9
eval9
modeling9
image-preprocessing9
splitting9
numpy7
Programming languages (1)
Github contributions (5)
BMIRDS/deepslide
Oct 2019 - Oct 2019
Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:16 commits, 1 PR, 16 pushes in 1 day
Contributions summary:Jerry appears to have contributed to the development of a deep learning pipeline for medical image analysis, specifically focusing on lung adenocarcinoma histology classification. Their commits involve the creation of scripts to split data, process image patches, train a ResNet model, and perform testing and evaluation. The user also implemented a grid search for hyperparameter tuning and visualized the results, demonstrating proficiency in model training, evaluation, and result interpretation within the context of the project.
patternshistopathologyclassificationreportsslide
JerryWeiAI/COVID-Q
May 2020 - Jan 2021
Contributions:51 commits, 48 pushes, 1 branch in 8 months
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