Haoming Guo is a Machine Learning Engineer at Apple with a decade of experience building and researching scalable ML systems, currently focused on RAG, Siri search ranking, and LLMs. He holds an MS in Computer Science from UC Berkeley and brings a strong research background from UC Berkeley and Microsoft Research Asia in unsupervised learning, robustness, and efficient transformer architectures. Haoming has a track record of turning research into production-ready models—designing custom transformers with graph attention for semi-structured data and contributing to speech synthesis and efficient transformer projects. He also contributes to open-source engineering, improving Turf.js by refactoring to TypeScript and hardening geospatial test coverage, showing attention to code quality beyond core ML work. Based in Berkeley, he combines rigorous academic training with product-focused engineering and a knack for pragmatic, hands-on implementation.
10 years of coding experience
4 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at University of California, Berkeley
High School Diploma, High School Diploma at Shenzhen Middle School
Chinese, English
Github Skills (9)
typescript10
javascript10
typescripts10
geospatial10
geojson10
typescript-types10
testing9
algorithm9
mappings8
Programming languages (9)
TypeScriptC#QMLC++JavaScriptHTMLJupyter NotebookRich Text Format
A modular geospatial engine written in JavaScript and TypeScript
Role in this project:
Full-stack Developer
Contributions:38 commits, 12 PRs, 5 comments in 11 days
Contributions summary:Haoming primarily contributed to the `turfjs/turf` repository by fixing comment typos and refactoring code to TypeScript. They also worked on test cases, including for geospatial analysis and random point generation. The user's commits show a focus on maintaining code quality and improving the functionality of the library.
Contributions:20 pushes, 1 branch in 5 years 3 months
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