Brent Payne

Sr. Manager, Machine Learning Applications, Personalization

Seattle, Washington, United States
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts
email-iconphone-icongithub-logolinkedin-logotwitter-logostackoverflow-logofacebook-logo
Join Prog.AI to see contacts

Summary

👤
Senior
🎓
Top School
Brent Payne is a seasoned machine learning leader with 14+ years of experience building production personalization systems, currently opening and managing ML applications teams in NYC and Seattle for Amazon. He blends hands-on engineering roots—evidenced by open-source contributions optimizing core Node.js stream primitives—with strategic product leadership from co-founding ML-forward startups and leading ML teams at ad-tech and B2B companies. Brent's career spans research, systems engineering, and go-to-market pivots, giving him a rare fluency in turning ML research into scalable, user-facing recommendations. He holds an M.S. in Artificial Intelligence from UCSD and is known for tackling performance and edge-case robustness in both legacy and modern runtime environments.
code14 years of coding experience
job13 years of employment as a software developer
bookUniversity of California San Diego
bookJohns Hopkins University
bookBachelor's degree, Computer Science, Bachelor's degree, Computer Science at University of Kentucky
github-logo-circle

Github Skills (10)

nodejs10
javascript10
functional-programming10
optimization9
optimisation9
numerical-optimization9
algorithm9
unit-testing9
code-optimization9
algorithms9

Programming languages (5)

TypeScriptC++JavaScriptHTMLPython

Github contributions (5)

github-logo-circle
caolan/highland

Apr 2015 - May 2015

High-level streams library for Node.js and the browser
Role in this project:
userBack-end Developer
Contributions:12 commits, 1 PR, 22 comments in 1 month
Contributions summary:Brent primarily focused on optimizing and refactoring the `pick` and `pickBy` methods within the `highland` streams library. They reduced the runtime complexity of these methods and reimplemented them using more efficient techniques like `map` and `curry`. The user also addressed edge cases by updating tests and handling non-enumerable properties. Furthermore, they updated the code to work correctly in both ES5 and ES3 environments.
browsernode-jsjavascripthigh-levelstreams
brentpayne/kennyg

Mar 2014 - Oct 2014

Contributions:19 commits in 6 months
saxjazzysax-parserneedparser
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial
Brent Payne - Sr. Manager, Machine Learning Applications, Personalization