Tuan Truong
Senior ML Engineer at Pinterest
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolTuan Truong is a Senior ML Engineer with 12 years of experience specializing in personalization, search, and recommender systems, currently building user signals and models for personalized feed and messaging at Pinterest. Previously at Amazon he led efforts on LLM-powered shopping guides and advanced personalization for search and recommendations across retail, moving research into production. He holds a PhD in Computer Science from Singapore Management University and a BSc from Vietnam National University, Hanoi, combining deep academic grounding with hands-on engineering. An active contributor to recommender-system best practices, he integrated and tested the Cornac library in a widely referenced recommenders repo, including utilities and a BPR deep-dive notebook. Tuan blends rigorous modeling expertise with pragmatic system design, favoring solutions that scale in high-throughput consumer products. Colleagues describe him as a research-driven engineer who turns complex personalization signals into measurable product impact.
12 years of coding experience
4 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at Singapore Management UniversityBSc, Computer Science, BSc, Computer Science at Vietnam National University, Hanoi
Vietnamese, English
Github Skills (12)
machine-learning10
jupyter-notebook10
recommendation-engine10
recommender10
recommendation-system10
python10
data-science10
recommender-system10
testing9
ai9
deep-learning8
deeplearning-ai8
Programming languages (5)
Github contributions (5)
recommenders-team/recommenders
Sep 2019 - Apr 2021
Best Practices on Recommendation Systems
Role in this project:
Data Scientist
Data ScientistContributions:6 reviews, 25 commits, 8 PRs in 1 year 6 months
Contributions summary:Tuan primarily contributed to the integration and testing of the Cornac library within the recommender system project. Their work included adding Cornac as a requirement, implementing utility functions (`cornac_utils.py`) for prediction, and writing unit tests to validate the functionality of the Cornac integration. Additionally, the user added a deep dive notebook demonstrating the use of Cornac's BPR model.
recommendation-systemspythonjupyter-notebookoperationalizationmicrosoft
qiuwei/FlexCRFs
Oct 2015 - May 2018
Contributions:5 commits in 2 years 7 months
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