Mingyu Li
Senior Software Engineer at Databricks
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMingyu Li is a Senior Software Engineer with a decade of experience building scalable systems across Databricks, Meta, and IBM, currently based in Menlo Park. He blends deep backend expertise in Java, Linux, and distributed services with hands-on ML engineering—contributing to the widely used mlflow project where he improved artifact viewing and pipeline robustness for Spark models. As a former Senior Staff Engineer and tech lead, he has driven cross-functional delivery and database/pipeline improvements at scale. Trained in electrical and computer engineering in Beijing and at the University of Maryland, he brings a hardware-aware, systems-oriented perspective to software problems. Colleagues rely on him for pragmatic engineering that tightens reliability while improving developer UX.
11 years of coding experience
8 years of employment as a software developer
BS, Electrical, Electronics and Communications Engineering, BS, Electrical, Electronics and Communications Engineering at Beijing University of Post and TelecommunicationsThe University of Maryland, College Park
Chinese
Github Skills (9)
apache-spark10
machine-learning10
mlflow10
model-management10
python10
sqlalchemy9
pytest8
react7
javascript7
Programming languages (5)
Github contributions (5)
mlflow/mlflow
Sep 2022 - Oct 2022
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
Role in this project:
ML Engineer
ML EngineerContributions:36 reviews, 10 commits, 25 PRs in 1 month
Contributions summary:Mingyu primarily contributes to the MLflow project by modifying existing code and adding new features to enhance the ML workflow. They implemented changes to the artifact viewer, particularly when dealing with models loaded using the mlflow.spark module, improving the user experience. Further contributions include modifying pipeline configurations, database interactions, and unit tests to improve the system's robustness. These changes also include improvements to the pipeline functionality and custom metric calculation.
aimlflowmlmodelmachine-learningml
mingyu89/mlops-stack
Nov 2022 - Nov 2023
Contributions:116 pushes, 59 branches in 1 year
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