Vivek Narayanan
Machine Learning at Meta
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolVivek Narayanan is a senior machine learning engineer with 15 years of experience based in the San Francisco Bay Area, currently leading ML efforts at Meta Superintelligence Labs. He has driven product-impacting ML work across growth, Reels recommendations, notifications, and on-device creation features, and he pioneered the first production reinforcement learning systems at Facebook to optimize long-term user value. He combines research-minded techniques in NLP and RL with pragmatic systems engineering—building frameworks that allowed RL to scale across multiple product teams. His open-source sentiment repo shows hands-on expertise in classical NLP methods, feature engineering, and model optimization, reflecting a penchant for interpretable, diagnostic-driven development. Trained with an ScM in Computer Science from Brown, he balances deep technical leadership with a knack for turning novel ML ideas into measurable product outcomes.
15 years of coding experience
2 years of employment as a software developer
ScM Computer Science, ScM Computer Science at Brown University
Github Skills (10)
sentiment-analysis10
machine-learning10
nlp10
feature-engineering10
naive-bayes10
python10
natural-language-processing10
data-analysis6
scikit4
scikit-learn4
Programming languages (3)
Github contributions (5)
vivekn/sentiment
Oct 2012 - Aug 2014
Sentiment analysis using machine learning techniques.
Role in this project:
ML Engineer
ML EngineerContributions:18 commits, 2 comments, 2 issues in 1 year 10 months
Contributions summary:Vivek primarily contributed to sentiment analysis using machine learning techniques, as indicated by the repository description. Their commits involved implementing and refining a naive Bayes classifier, incorporating POS-based upscaling, and integrating a pretrained version. The contributions included refactoring code, adding diagnostic tools, and experimenting with bigrams and mutual information for feature selection and performance improvements, demonstrating a focus on model optimization.
nlptechniquesmachine-learningsentimentsentiment-analysis
vivekn/nnet
May 2013 - May 2015
Contributions:3 commits, 1 push in 2 years
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