Wei Yang
Senior Machine Learning Engineer at Databricks
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolWei Yang is a Senior Machine Learning Engineer with 11 years of experience building recommendation, search, and ranking systems for high-scale consumer products. Based in San Jose, he has driven feed quality, long-term user experience, and content safety at TikTok and delivered personalized ranking and embedding solutions for Wish’s multi-million-DAU commerce platform. His background blends research and production: prior roles span text-to-SQL and QA systems at Borealis AI and RSVP.ai, and he holds a CS master’s from Waterloo. Wei contributes to notable open-source IR tooling such as Anserini, where he improved back-end search components and RM3 reranking support—reflecting deep IR expertise beyond typical recommender work. Currently at Databricks, he applies LLM-based annotation and fine-tuning techniques to practical ML pipelines, bridging model research and robust engineering. Colleagues describe him as a pragmatic engineer who moves novel ML ideas into reliable, test-covered production systems.
11 years of coding experience
7 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Zhejiang UniversityExchange Student & Research Assistant Computer Science, Exchange Student & Research Assistant Computer Science at Singapore University of Technology and Design (SUTD)Master's degree Computer Science, Master's degree Computer Science at University of Waterloo
Github Skills (6)
lucene10
javas10
information-retrieval10
java10
testing10
python9
Programming languages (6)
Github contributions (5)
castorini/anserini
Jun 2018 - Jul 2019
Anserini is a Lucene toolkit for reproducible information retrieval research
Role in this project:
Back-end Developer
Back-end DeveloperContributions:18 commits, 19 PRs, 3 pushes in 1 year 1 month
Contributions summary:Wei primarily contributed to the back-end functionality of the Anserini project, focusing on information retrieval research. Their work involved refactoring code, updating topic readers, and implementing new collection types like CAR18 and adding support for RM3 reranking. The user also modified the SimpleSearcher class to support different bag-of-words models and incorporate the RM3 reranker, improving search capabilities. Additionally, the user addressed bug fixes and added regression tests to ensure the reliability of the search functionalities.
information-retrievallucene
Victor0118/NCE_SM_Pytorch
Oct 2017 - Jan 2018
Contributions:35 commits, 1 PR, 49 pushes in 2 months
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