Bryan Qiu
Software Engineer at Databricks
Greater Chicago Area United States
Summary
🤩
Rockstar🎓
Top SchoolBryan Qiu is a software engineer with six years of experience building ML and platform infrastructure, currently working on MLflow and the Agent Platform at Databricks. He brings hands-on expertise in model management, PySpark integrations, and improving developer experience—evidenced by notable contributions to the widely used open-source mlflow project. Bryan’s internships across fintech, cloud, and developer tooling (Plaid, Anyscale, Retool, Jump Trading) reflect strong versatility across backend systems and payments/platform domains. A UC Berkeley EECS graduate with a near-perfect GPA, he combines academic rigor with practical product shipping. He has a knack for clarifying and documenting complex ML workflows, making advanced features more accessible to users. Based in the Greater Chicago Area, he’s actively seeking software engineering roles where he can bridge ML tooling and production-grade engineering.
6 years of coding experience
4 years of employment as a software developer
EECS, Computer Science, 3.95, EECS, Computer Science, 3.95 at University of California, BerkeleyHigh School Diploma, 4.7/4.0, High School Diploma, 4.7/4.0 at Neuqua Valley High School
Spanish, Chinese, English
Github Skills (9)
apache-spark10
machine-learning10
pyspark10
model-management10
mlflow10
python10
langchain10
documentation9
ai9
Programming languages (4)
Github contributions (5)
mlflow/mlflow
Aug 2023 - Mar 2025
Open source platform for the machine learning lifecycle
Role in this project:
ML Engineer
ML EngineerContributions:150 reviews, 60 PRs, 12 pushes in 1 year 7 months
Contributions summary:Bryan primarily contributed to the `mlflow/mlflow` repository by implementing and refining features related to machine learning model management. Their commits focused on improving the integration with various machine learning frameworks, like PySpark, and enhancing the documentation to improve usability. The contributions include adding type annotations, updating documentation to reflect outputs, and supporting complex scenarios for LangChain model logging. These changes show a focus on improving the user experience and expanding the functionality of model logging and deployment within MLflow.
pythonlifecyclemlmachine-learningincremental-learning
bbqiu/mlflow
Aug 2023 - Apr 2025
Open source platform for the machine learning lifecycle
Contributions:2 PRs, 634 pushes, 47 branches in 1 year 7 months
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