Chunxu Tang is a staff software engineer and researcher with 11 years of experience building large-scale data and AI infrastructure, currently focused on model serving, vector analytics, and retrieval systems. He has driven core AI infra at LinkedIn and contributed cloud-native data access and caching solutions at Alluxio that boosted GPU utilization to 90%+ for enterprise ML workloads. A long-time open-source committer to PrestoDB—where he has improved the engine’s front-end and tooling—he also helped evolve Twitter’s Presto deployment that processed ~10 PB/day. His work spans distributed systems, query processing, and ML-powered pipelines, and earned the IEEE IC2E Best Industry Track Paper Award for industry-impactful ML-driven data systems. Based in Mountain View with a Ph.D. in Computer Engineering, Chunxu combines deep research rigor with production-scale engineering and cross-company collaboration (e.g., partnerships with Uber) to close the gap between ML research and deployable infrastructure.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Engineering, Doctor of Philosophy (Ph.D.) Computer Engineering at Syracuse University
Bachelor of Engineering (B.E.), Bachelor of Engineering (B.E.) at Xiamen University
The official home of the Presto distributed SQL query engine for big data
Role in this project:
Front-end Developer
Contributions:191 reviews, 58 commits, 53 PRs in 2 years 7 months
Contributions summary:Chunxu primarily focused on improving the user interface for the Presto distributed SQL query engine. Their work involved implementing a production mode for building the web distribution, refactoring strings using template literals, and formatting quotes in the webpack configuration file. Additionally, they modified the code to format the quotes of mode to single quotes and double quotes in the getChildren() method.
Contributions:22 reviews, 9 PRs, 36 pushes in 8 months
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