Jared Wilber
Senior Software Engineer at UC San Diego
San Diego, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJared Wilber is a Senior Software Engineer with nine years of experience building machine learning systems, data visualization tooling, and developer-facing infrastructure, currently working on BioNeMo and NIMs at NVIDIA. He combines research and product instincts—teaching data science at UC San Diego while bringing practical ML features to production from roles at AWS and a YC S23 startup he co-founded. His open-source contributions range from web-friendly ML (implementing K-Means in ml5js) to creative data viz (adding accessible charts to roughViz), showing fluency across front-end and ML stacks. Jared has a strong background deploying ML models into utility-scale production and an academic foundation from UC Berkeley in Statistics and Computer Science. Colleagues know him for bridging prototype research and reliable production code, and for shipping thoughtful accessibility and tooling improvements that quietly raise product quality.
9 years of coding experience
9 years of employment as a software developer
Bachelor's degree Statistics Computer Science, Bachelor's degree Statistics Computer Science at University of California, BerkeleyS23, S23 at Y Combinator
English, Spanish
Stackoverflow
6,436reputation
2.6mreached
58answers
10questions
css
top-5%
dataframe
top-5%
anaconda
top-5%
matplotlib
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macos
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yaml
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Github Skills (34)
data-visualizations10
javascript10
charts10
tensorflowjs10
machine-learning10
data-visualisation10
kmeans10
chartjs10
charting10
data-visualization10
d3js10
matplotlib9
pandas9
github9
dataframe9
Programming languages (8)
Github contributions (5)
jwilber/roughViz
Sep 2019 - Oct 2021
Reusable JavaScript library for creating sketchy/hand-drawn styled charts in the browser.
Role in this project:
Front-end Developer
Front-end DeveloperContributions:106 commits, 49 PRs, 81 pushes in 2 years 1 month
Contributions summary:Jared primarily contributed to the `jwilber/roughviz` repository by adding and modifying charts within the browser. Their work involved implementing various chart types, including Scatter, Bar, Donut, and Pie charts, demonstrating a focus on data visualization techniques. This work involved adding example usages and also included the implementation of some accessibility features within the charts. Additionally, the user was involved in test initialization.
hand-drawnd3v5dashboardchartsbrowser
ml5js/ml5-library
Aug 2019 - Aug 2019
Friendly machine learning for the web! 🤖
Role in this project:ML Engineer
ML EngineerContributions:8 commits, 1 PR, 9 comments in 5 days
Contributions summary:Jared primarily contributed to the `ml5-library` by implementing the K-Means clustering algorithm. The commits include the creation of a `KMeans` class, along with related utility functions such as `randomSample`. The user's work demonstrates a focus on machine learning techniques and the integration of TensorFlow.js for web-based model implementation.
imagenetdeep-learninglstmjavascriptp5xjs
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