Andrew Shaw
Research Scientist (AI Music) at TikTok
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrew Shaw is a research scientist specializing in AI-driven music systems, bringing 14 years of software and research experience from startups to large platforms in the San Francisco Bay Area. Currently at TikTok, he blends deep learning research with practical product engineering and has founded a music tech startup (MusicAutobot) that reflects his focus on democratizing creative tools. His background spans mobile-first engineering—early work as Astrid’s first engineer and iOS/Android contributions—to documenting and teaching deep learning via fast.ai, highlighting both implementation and communication strengths. Andrew’s track record includes infrastructure work at Scale AI and academic research roles, giving him rare fluency across research, production, and developer education. Notably, he contributes UI improvements to widely used mobile apps and writes technical documentation that makes complex ML tools more accessible.
14 years of coding experience
11 years of employment as a software developer
Computer Science + Entrepreneurship, Computer Science + Entrepreneurship at Washington University in St. Louis
Github Skills (9)
javas10
user-interface10
android-sdk10
android10
java10
documentation10
ui-design9
html9
jekyll8
Programming languages (7)
Github contributions (5)
todoroo/astrid
Jan 2012 - Feb 2012
Astrid: Android's #1 Task Management Application
Role in this project:
Mobile Developer (Android)
Mobile Developer (Android)Contributions:87 commits in 1 month
Contributions summary:Andrew primarily contributed to the Android application, implementing and modifying UI elements and layouts. Their work included adding new checkbox images, adapting the task adapter, and making enhancements to the user interface. They also worked on features related to task details and repeat functionality, further indicating a focus on improving the user experience within the Astrid app.
android
fastai/fastai_dev
Sep 2018 - Oct 2018
fast.ai early development experiments
Role in this project:Technical Writer
Technical WriterContributions:28 commits, 5 PRs, 18 pushes in 25 days
Contributions summary:Andrew's commits focus on updating and adding documentation to the fastai_dev repository. They updated the learning rate finder documentation, fixed linking issues in the vision and index documentation, updated rest of index tabulear and collab links, added anchors to page titles, added show_image to vision.image, updated vision source links, and documented config changes. The commits primarily involve modifications to documentation notebooks, and HTML files which suggest the user is focused on providing accurate and clear explanations of the repository's functionalities.
pythondata-sciencemachine-learningfastainbdev
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