Lex Yu
MTS 2 at eBay
Shanghai, Shanghai, China
Summary
👤
Senior🎓
Top SchoolLex Yu is an AI and big data architect with over a decade of hands‑on experience building large-scale data platforms and production ML systems, currently leading AI and MLOps architecture at eBay in Shanghai. He has driven platform projects from a unified feature store and an enterprise analytics platform used by 1,000+ users to a training/inference and LLM stack built on MLflow and Ray, and spoke at Ray Summit in 2025 and 2026. A prolific performance optimizer, Lex contributed 100+ PRs to Apache Spark—adding Parquet DateType pushdown, a weekday UDF, and key execution optimizations—and previously led adaptive execution and tiered storage features at Intel that produced substantial SQL and ML speedups. He combines deep systems expertise (NUMA, RDMA, Optane, FPGA) with product-scale delivery, having automated migration of 60PB of warehouse data to Spark and cut memory usage in half through framework tuning. Colleagues rely on him for solving thorny production performance and data‑skew problems that most teams would avoid.
11 years of coding experience
12 years of employment as a software developer
Bachelor's Degree, Computer Software Engineering, Bachelor's Degree, Computer Software Engineering at Xiamen UniversityMaster’s Degree, Computer Science, Master’s Degree, Computer Science at Shanghai Jiao Tong University
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
parquet10
javas10
big-data10
spark10
sql10
java10
scala10
performance-optimization9
algorithms8
data-structures8
data-structure8
Programming languages (4)
Github contributions (5)
apache/spark
Apr 2015 - Nov 2019
Apache Spark - A unified analytics engine for large-scale data processing
Role in this project:
Back-end Developer
Back-end DeveloperContributions:39 PRs, 232 comments in 4 years 7 months
Contributions summary:Lex primarily contributed to the Apache Spark project by implementing and optimizing features related to data processing and query execution. Their work includes adding support for pushing down filters for DateType in Parquet files, which improves query performance. The user also added the `weekday` UDF (User-Defined Function), enhancing the SQL capabilities within Spark. Further contributions involved addressing hash conflicts and optimizing output partitioning within the query execution engine, showing a focus on performance improvements.
analyticspythondata-processingsqlapache
yucai/ray
Jun 2024 - Jun 2024
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Contributions:21 pushes, 1 branch in 2 days
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