Kate Silverstein

Staff Machine Learning Engineer at Mozilla

Greater Boston United States
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Summary

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Kate Silverstein is a Staff Machine Learning Engineer based in Greater Boston with 12 years of experience building production ML systems across industry and research. She has led ML work at Mozilla, Fidelity, Twitter, and Oracle, bringing expertise in NLP and deployable probabilistic modeling from both research labs and large-scale product teams. Her open-source contributions include substantive maintenance and lexicon development for the FACTORIE Scala toolkit, including support for Mandarin name lexicons—evidence of careful, multilingual-aware engineering. With an M.S. in Computer Science and a background in linguistics, she combines rigorous academic foundations with practical shipping experience and a knack for cleaning up and hardening complex codebases.
code12 years of coding experience
job14 years of employment as a software developer
bookMaster of Science (M.S.) Computer Science, Master of Science (M.S.) Computer Science at University of Massachusetts Amherst
languagesEnglish, Spanish
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Github Skills (4)

scala10
nlp9
refactoring8
testing7

Programming languages (9)

JavaC++ScalaTeXJavaScriptLuaHTMLJupyter Notebook

Github contributions (5)

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factorie/factorie

May 2014 - Nov 2015

FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
Role in this project:
userBack-end Developer
Contributions:38 commits, 9 PRs, 1 comment in 1 year 5 months
Contributions summary:Kate primarily contributed to the `factorie/factorie` repository by modifying and refactoring lexicon-related code. Their commits involved renaming classes, updating references, and restructuring lexicon implementations. They also added new lexicons, including ones for Mandarin Chinese names. Furthermore, the user removed commented-out code, added test coverage, and corrected merge conflicts, indicating a focus on code maintenance and improvement.
library-softwareparametersrelationaldeployableprobabilistic-modeling
iesl/bibie

Mar 2015 - Mar 2016

Contributions:130 commits, 1 PR, 81 pushes in 1 year
nlpextractionheaderfieldnatural-language-processing
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Kate Silverstein - Staff Machine Learning Engineer at Mozilla