Yue Zhang
Senior Research Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYue Zhang is a Senior Research Engineer based in San Francisco with 12 years of experience building production ML systems and developer-facing data tooling. At Databricks they lead work on natural language to SQL and retrieval-augmented generation, and previously drove AutoML initiatives including distributed hyperparameter tuning and fine-tuning of language models for embeddings and text classification. Their background spans research (model-based RL at Berkeley AI Research), full-stack ML product development, and hands-on data engineering—evidenced by contributing Databricks Connect integration and query-plan tooling to the popular sparklyr project. Yue combines deep algorithmic understanding from a UC Berkeley EECS degree with operational experience building runtimes, services, and React frontends. Colleagues rely on them to bridge research and production, shipping novel ML features end-to-end. An underappreciated strength is their history of teaching and curriculum development, which helps them explain complex systems clearly to engineers and stakeholders.
12 years of coding experience
8 years of employment as a software developer
Bachelor of Science (B.S.) Electrical Engineering and Computer Science, Bachelor of Science (B.S.) Electrical Engineering and Computer Science at University of California, BerkeleyHenry M. Gunn High School
Github Skills (6)
apache-spark10
r10
cicd9
jenkins9
dplyr8
machine-learning6
Programming languages (8)
Github contributions (5)
sparklyr/sparklyr
Nov 2019 - Aug 2020
R interface for Apache Spark
Role in this project:
Data Engineer
Data EngineerContributions:10 commits, 18 PRs, 22 comments in 9 months
Contributions summary:Yue primarily contributed to improving the integration and testing of the `sparklyr` package with Databricks Connect. Their work involved implementing and refining the connection method for Databricks, addressing serialization issues, and incorporating Databricks-specific configurations. Additionally, they added new functionality such as `sdf_query_plan` to retrieve optimized/analyzed query plans. They also updated the CI and added Databricks test cases.
rsparklyrmachine-learningrstatsspark
Loquats/cs184-snow
Apr 2018 - Nov 2019
Contributions:57 commits, 3 pushes in 1 year 7 months
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