Kyle Phillips
Software Engineer at Google
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolKyle Phillips is a software engineer and creative technologist with 15 years of experience designing and shipping AI-driven products that connect research, design, and real-world users. At Google Creative Lab he architected and built flagship projects like Teachable Machine (including a patented node-based classifier) and Creatability, blending no-code ML, accessibility, and web-first inference with TensorFlow.js. He excels in the "messy middle"—translating ambiguous research into polished interfaces and platforms adopted by millions—and recently produced the popular Gemini Live API Web Console. A hands-on contributor to open-source testing and ML tooling, Kyle pairs full-stack engineering chops with a practiced designer’s sensibility, rooted in a BFA in Interactive Media and a history of cross-disciplinary collaboration.
15 years of coding experience
13 years of employment as a software developer
A.A.S., Graphic Communications, A.A.S., Graphic Communications at Kirkwood Community CollegeB.F.A., Interactive Media, B.F.A., Interactive Media at Minneapolis College of Art and Design
Github Skills (12)
typescript10
javascript10
typescripts10
typescript-types10
k10
testing10
mochajs9
chai9
puppeteer9
machine-learning9
webpack8
webpacker8
Programming languages (6)
Github contributions (5)
googlecreativelab/teachablemachine-community
Jun 2019 - May 2020
Example code snippets and machine learning code for Teachable Machine
Role in this project:
ML Engineer
ML EngineerContributions:41 commits, 16 PRs, 25 pushes in 10 months
Contributions summary:Kyle contributed to the setup and configuration of testing frameworks like Karma for the "teachablemachine-community" repository. They worked on integrating the Karma testing framework with Typescript, Mocha, and Chai for testing the image-related functionality. The user's commits included modifying test files, updating configuration files, and integrating the test suite with the build and deployment process using puppeteer for ChromeHeadless. These modifications likely support the testing of the machine-learning model and its associated code.
pythontensorflowjsteachablemachine-learningsnippets
hapticdata/Sketchplate
Apr 2012 - Dec 2018
Contributions:189 commits, 19 pushes, 1 branch in 6 years 9 months
amdboilerplatenode-jsexcellentjavascript
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