Sue Hong
Software Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSue Hong is a software engineer with a PhD in Computer Science from Carnegie Mellon and nine years of experience building production ML and MLOps systems, currently at Databricks in San Francisco. She has bridged research and production throughout her career—moving from distributed multi-agent planning research to shipping real-time model serving, MLflow integrations, and deep-learning pipelines for Spark. Her open-source contributions include enhancing MLflow deployments (SageMaker/AzureML) and implementing Keras-based image featurization models in the widely used databricks/spark-deep-learning project. Comfortable both leading teams and writing production code, she blends rigorous academic foundations with hands-on engineering to simplify model deployment at scale. An under-the-radar strength is her background in optimization and distributed algorithms, which informs robust, convergent solutions for complex ML systems.
9 years of coding experience
13 years of employment as a software developer
California Institute of TechnologyThe Webb SchoolsDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Carnegie Mellon University
English, Korean
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (19)
continuous-deployment10
apache-spark10
python10
machine-learning10
ml-deployment10
keras10
mlops10
deep-learning10
tensorflow10
computer-vision10
image-processing9
azure-machine-learning9
transformers9
dockers9
docker9
Programming languages (7)
Github contributions (5)
databricks/spark-deep-learning
Jun 2017 - Feb 2018
Deep Learning Pipelines for Apache Spark
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 33 PRs, 24 pushes in 8 months
Contributions summary:Sue primarily contributed to the development of deep learning models within the Spark environment. Their work included implementing Keras application models (InceptionV3, Xception, ResNet50) for image featurization and prediction. The contributions involved modifying existing transformers, adding support for new models, and integrating them into the SparkDL pipeline for image processing and feature extraction. They also made improvements to the testing framework and added licensing information for used models.
data-sciencedeep-learningmachine-learningapachespark
mlflow/mlflow
Jul 2018 - Nov 2020
Open source platform for the machine learning lifecycle
Role in this project:MLOps Engineer
MLOps EngineerContributions:2 releases, 44 reviews, 64 commits in 2 years 4 months
Contributions summary:Sue primarily contributed to the deployment and management aspects of the MLflow project. Their commits focused on enhancing the SageMaker deployment functionality, including tagging Docker images with the correct MLflow version and automating S3 bucket and execution role parameters. They also streamlined the AzureML deployment process. The user's work improved error messages related to experiment handling, and ensured that artifacts could be downloaded from a models:// URI.
pythonlifecyclemlmachine-learningincremental-learning
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