Daniel Smilkov
Member Of Technical Staff at Anthropic
Boston, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Smilkov is a founder and engineer with 12 years of experience building production ML systems and developer tooling from Boston. He co-founded Lilac AI (acquired by Databricks) and co-created TensorFlow.js after stints at Google Brain, PAIR, and the MIT Media Lab. His work spans end-to-end JavaScript ML—authoring and optimizing WebGL-accelerated kernels, model converters and tfjs-node deployment, and improving CI and testing infrastructure. He’s comfortable crossing front-end and back-end boundaries, contributing UI work like the Deep Playground and algorithmic improvements in d3plus while also optimizing WebGL samplers and memory management to make browser ML practical. An active open-source leader, he blends research-grade ideas with production-focused engineering to move models from experiment to scalable deployment.
12 years of coding experience
14 years of employment as a software developer
Master of Science (M.Sc.), MIT Media Lab, Master of Science (M.Sc.), MIT Media Lab at Massachusetts Institute of TechnologyBachelor, Computer Science, Bachelor, Computer Science at Faculty of Computer Science and Engineering - Skopje
English
Stackoverflow
480reputation
21kreached
4answers
2questions
Github Skills (64)
algorithm10
data-visualizations10
webgl10
d3-js10
algorithms10
typescript310
javascript10
npm10
node-js10
coffeescript10
back-end-development10
python10
network-analysis10
datastructure10
machine-learning10
Programming languages (11)
Github contributions (5)
tensorflow/tfjs-core
Aug 2017 - Aug 2019
WebGL-accelerated ML // linear algebra // automatic differentiation for JavaScript.
Role in this project:
Back-end Developer & Machine Learning Engineer
Back-end Developer & Machine Learning EngineerContributions:6 releases, 413 commits, 776 PRs in 2 years
Contributions summary:Daniel contributed to the development of a hardware-accelerated machine intelligence library for JavaScript. Their work involved implementing new features, such as adding support for string weights and enabling more tensor operations. The user also focused on performance improvements by optimizing the WebGL samplers and restructuring the code for better memory management, crucial for the efficiency of the deep learning library.
linear-algebraautomatic-differentiationdifferentiationwebgljavascript
tensorflow/playground
Mar 2016 - Aug 2021
Play with neural networks!
Role in this project:Front-end Developer
Front-end DeveloperContributions:121 commits, 21 PRs, 49 pushes in 5 years 5 months
Contributions summary:Daniel primarily focused on porting the "Deep Playground" application from Google3 to Git, making significant changes to the HTML and TypeScript files. They implemented UI improvements, including adding a gradient color map and adjusting the height of the main parts dynamically. Additionally, they linked label values with input range controls and added hover effects to buttons.
deep-learningneural-networksmachine-learningneural-networktensorflow
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