Jun Min
Senior Software Engineer at Meta
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJun Min is a Senior Software Engineer and AI/ML specialist with a PhD in Computer Science and over a decade of experience building end-to-end AI systems across LLMs, computer vision, and recommender systems. He has led production-grade ML work at Microsoft and now Meta, combining research rigor from a CMU postdoc with hands-on engineering that spans data engineering, back-end reliability, and cloud deployments. A core contributor and maintainer of a highly starred open-source AI project and active in notable repos like Microsoft’s computervision-recipes and the recommenders project, he ships reproducible notebooks, unit tests, and dataset scaling improvements. His work includes practical systems fixes—Spark submission reliability, dataset parameterization, and webcam-based model demos—showing a focus on making ML work in real environments. Based in Los Angeles, Jun blends academic depth with product-driven execution and a playful engineer’s culture hinted at by a GitHub bio that prefers coffee and music as prerequisites for work.
7 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Yonsei University
Korean, English
Github Skills (24)
unit-testing10
apache-spark10
pytorch10
recommendation-system10
python10
load-data10
data-science10
image-classification10
data-set10
data-engineering10
pandas10
machine-learning10
feature-engineering10
datasets10
data-loading10
Programming languages (10)
Github contributions (5)
recommenders-team/recommenders
Oct 2018 - Jan 2023
Best Practices on Recommendation Systems
Role in this project:
ML Engineer
ML EngineerContributions:54 reviews, 283 commits, 56 PRs in 4 years 3 months
Contributions summary:Jun added and parameterized different MovieLens datasets, including 100k, 1m, 10m, and 20m versions, within a quickstart notebook focused on the SAR (Smart Adaptive Recommendations) algorithm. The changes involved modifications to the notebook to accommodate larger datasets. Additionally, the user made code adjustments to handle different data formats specific to the chosen dataset sizes.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
microsoft/computervision-recipes
Feb 2019 - Nov 2019
Best Practices, code samples, and documentation for Computer Vision.
Role in this project:ML Engineer
ML EngineerContributions:43 commits, 9 PRs, 10 pushes in 8 months
Contributions summary:Jun implemented a webcam image classification quickstart notebook using the fastai library. The notebook demonstrates loading a pre-trained ResNet18 model and using it to classify images from a webcam stream. The user added a unit test for the notebook's core functionality. The changes included modifications to the code, addition of screenshot and other minor updates.
pythonjupyter-notebookoperationalizationimage-classificationmicrosoft
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