Chao Xiang is a back-end developer with 10 years of experience building scalable data-driven systems, currently contributing to ByteDance from Los Angeles. He has deep practical experience optimizing large-scale distributed processing—improving Hadoop cluster throughput fivefold and refactoring PySpark transformers for parallel efficiency. Earlier roles include developing Hive UDFs and Spark/TF models for petabyte-scale user data, demonstrating a strong bridge between data engineering and applied machine learning. Academically grounded with degrees in Computer Science and Artificial Intelligence and graduate study at USC, he combines rigorous academics with hands-on production impact. Colleagues describe him as someone who enjoys probing the mechanisms behind systems, turning performance bottlenecks into reliable, measurable gains. He brings a pragmatic focus on efficiency and tooling that helps teams operate large data platforms more effectively.
10 years of coding experience
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Southern California
Bachelor of Science - BS, Artificial Intelligence, 3.93, Bachelor of Science - BS, Artificial Intelligence, 3.93 at University of Liverpool
Bachelor of Science - BS, Computer and Information Sciences, General, 3.8, Bachelor of Science - BS, Computer and Information Sciences, General, 3.8 at Xi'an Jiaotong-Liverpool University
Summer Session, Statistics, 4.0, Summer Session, Statistics, 4.0 at University of California, Los Angeles
Contributions:2 pushes, 1 branch, 5 comments in 2 years 2 months
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