Steven Chen
Senior Software Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSteven Chen is a Senior Software Engineer in San Francisco with five years of experience building production ML infrastructure and model serving at Databricks. He combines a strong academic foundation in computer science and mathematics from UNC Chapel Hill with hands-on work across NLP research, AR eyetracking, and enterprise cloud engineering. At Databricks he has progressed from intern to senior engineer focusing on reliable model serving, and his open-source contributions to MLflow reflect a practical focus on reproducible model logging and artifact validation for PyTorch, TensorFlow, and Spark flavors. Colleagues rely on him to bridge research-quality model work and robust production workflows, and he brings a knack for improving testability and documentation that reduces deployment surprises.
4 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS Computer Science and Mathematics, Bachelor of Science - BS Computer Science and Mathematics at University of North Carolina at Chapel Hill
Github Skills (11)
continuous-deployment10
apache-spark10
pytorch10
model-management10
mlflow10
tensorflow10
ml-deployment10
testing9
cicd8
python8
ml7
Programming languages (2)
Github contributions (5)
mlflow/mlflow
Jun 2021 - Mar 2022
Open source platform for the machine learning lifecycle
Role in this project:
MLOps Engineer
MLOps EngineerContributions:23 reviews, 7 commits, 21 PRs in 9 months
Contributions summary:Steven primarily contributed to the project by enhancing testing procedures related to model exporting and model logging, specifically for the PyTorch, TensorFlow, and Spark flavors of the MLflow library. They implemented tests to ensure proper logging of dependencies like conda environments and pip requirements, ensuring the reproducibility of machine learning models. These changes focused on verifying correct model saving and loading behavior with various configurations, especially for the logging of ML model artifacts. Additionally, the user worked on updating the documentation and making the GetLatestVersions api.
pythonlifecyclemlmachine-learningincremental-learning
stevenchen-db/mlflow
May 2021 - Dec 2024
Open source platform for the machine learning lifecycle
Contributions:96 pushes, 15 branches in 3 years 7 months
pythonlifecyclemachine-learningmlopsdvc
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