Zhe Zhang
Distinguished Engineer at NVIDIA
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolZhe Zhang is a Distinguished Engineer with ~15 years building large-scale distributed systems and over seven years of experience growing and supporting engineering teams, now at NVIDIA DGX Cloud. He led the Ray project and helped drive its industrial adoption, and earlier architected HDFS Erasure Coding—feature work that materially reduced storage costs in Hadoop deployments. At LinkedIn he scaled core big-data infra (HDFS, YARN, Spark) and shipped innovations like observer reads that increased HDFS read scalability by ~4x. An Apache member and long-time Hadoop committer, he combines deep research roots (PhD) with hands-on open-source impact and strong mentorship skills. He is motivated by distributed systems, open source, and making AI-native infrastructure easier to build and operate.
11 years of coding experience
7 years of employment as a software developer
Ph.D. Computer Science and Operations Research, Ph.D. Computer Science and Operations Research at North Carolina State UniversityB.E. Computer Science, B.E. Computer Science at University of Science and Technology of ChinaHigh school diploma, High school diploma at Beijing 44 Middle School
Chinese, English
Github Skills (18)
filesystem10
blocks10
distributed-systems10
hadoop10
java10
javas10
coding10
manage10
encoding10
ray10
erasure10
documentation10
python9
cluster-computing9
clustering9
Programming languages (8)
Github contributions (5)
apache/hadoop
Jan 2015 - Jan 2018
Apache Hadoop
Role in this project:
Backend Developer
Backend DeveloperContributions:195 commits, 1 comment in 3 years
Contributions summary:Zhe primarily contributed to the Apache Hadoop project by implementing and modifying core backend features. Their work involved adding new features related to Erasure Coding, including processing block reports and distributing recovery tasks for striped blocks. Additionally, they made enhancements to file handling operations like lease recovery of striped block groups and made changes to support the correct calculation of file sizes with striped blocks.
apachebig-datasparkhadoopjava
ray-project/ray
Aug 2020 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:Technical Writer
Technical WriterContributions:617 reviews, 14 commits, 68 PRs in 2 years 4 months
Contributions summary:Zhe primarily contributed to the documentation of the Ray project. Their work involved fixing errors, clarifying examples, and adding new content to the documentation, including updates to the Ray Data, cluster, and RLlib documentation. They also updated the contribution guide and development instructions. These changes improved the clarity and accuracy of the project's documentation.
pythonconsistsruntimetensorflowserving
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