Daniel Smilkov
Member Of Technical Staff at Anthropic
Boston, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Smilkov is a machine learning engineer and researcher with 13 years of experience building developer-facing ML tools, visualization systems, and applied AI products. He co-created TensorFlow.js, helped port and modernize the influential TensorFlow Playground, and co-founded Lilac AI (acquired by Databricks) to improve data understanding for AI products. At Google Brain and PAIR he bridged research and production, shipping visualization work in TensorBoard and saliency tooling used across frameworks. More recently he worked on agent quality at Databricks and continues at Anthropic, bringing product-focused research to safety and developer tooling. Daniel blends deep technical skills across WebGL-accelerated ML, front-end UX for model interaction, and backend performance optimizations. An MIT-trained researcher, he has a knack for turning visualization and interpretability research into practical, widely adopted open-source software.
13 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 TechnologyM.Sc. Computer Science, M.Sc. Computer Science at Faculty of Computer Science and Engineering - Skopje
English
Stackoverflow
480reputation
21kreached
4answers
2questions
Github Skills (53)
algorithm10
data-visualizations10
webgl10
algorithms10
javascript10
npm10
coffeescript10
python10
network-analysis10
back-end-development10
tensorflowjs10
typescript10
machine-learning10
user-interface10
data-visualisation10
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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