John White
Director Of Engineering at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolJohn White is a Director of Engineering based in New York with 17 years of experience leading software teams and shipping production-grade systems at scale. He progressed through technical and managerial roles at Facebook/Meta, moving from software engineer to senior engineering manager and now director, demonstrating deep expertise in both hands-on development and people leadership. A longtime open-source contributor in the Julia ecosystem, he’s implemented core language features (Nullable types), statistical and optimization libraries, and ML tooling used by researchers and practitioners. His work spans algorithms (optimization methods, multi-armed bandits), statistical modeling (GLMs, distributions), and robust data structures for tabular data, signaling a rare blend of applied math and production engineering. He holds a PhD from Princeton in psychology and a BA in mathematics from Columbia, reflecting strong quantitative training applied to software and ML systems. Peers note he brings an engineer-first mindset to leadership, often contributing key algorithmic improvements himself rather than delegating.
17 years of coding experience
12 years of employment as a software developer
Bachelor of Arts - BA Mathematics, Bachelor of Arts - BA Mathematics at Columbia UniversityDoctor of Philosophy - PhD Psychology, Doctor of Philosophy - PhD Psychology at Princeton University
Github Skills (42)
algorithm10
data-visualizations10
algorithms10
summarization10
programming-language10
python10
optimizers10
data-science10
r10
package-development10
testing10
dataframes10
statistics10
machine-learning10
lm10
Programming languages (8)
Github contributions (5)
johnmyleswhite/BanditsBook
Oct 2012 - May 2016
Code for my book on Multi-Armed Bandit Algorithms
Role in this project:
Data Scientist / ML Engineer
Data Scientist / ML EngineerContributions:23 commits, 2 PRs, 2 pushes in 3 years 7 months
Contributions summary:John's primary contribution centers around implementing and testing various multi-armed bandit algorithms. The code modifications include defining and implementing several algorithms such as Exp3, Softmax, UCB1, and annealing variants of epsilon-greedy and softmax in both Python and Julia. Further contributions include the creation of arms with varied characteristics and a testing framework, indicating a focus on practical application and comparison of these bandit algorithms. The user also added basic plotting tools using R to visualize the results.
bandit-algorithmsarmed-banditbanditmulti-armed-bandit
johnmyleswhite/ML_for_Hackers
Feb 2012 - Jul 2013
Code accompanying the book "Machine Learning for Hackers"
Role in this project:Data Scientist
Data ScientistContributions:56 commits, 3 comments in 1 year 5 months
Contributions summary:John primarily worked on code examples for the book "Machine Learning for Hackers," focusing on various machine learning concepts. Their contributions included implementing and comparing different regression models (linear regression, polynomial regression) and regularization techniques. They demonstrated skills in data analysis, model building, and visualization using R, along with exploring techniques like cross-validation and model comparison.
pythondata-sciencemachine-learning-bookmachine-learninghackers
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