Miyoung Choi
Senior Technical Writer at NVIDIA
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMiyoung Choi is a Senior Technical Writer based in San Francisco with nine years of experience specializing in AI/ML documentation, content engineering, and developer-focused UX. She has shaped complex developer workflows at AWS SageMaker—documenting distributed training, debugging, profiling, and compiler tooling—and now authors NeMo/NIM docs at NVIDIA. Her background as a PhD-trained researcher and graduate teaching awardee gives her a rare blend of deep technical rigor and clear pedagogy for audiences from researchers to production engineers. Miyoung is also an active open-source contributor, improving SageMaker SDK examples and debugger docs to make advanced ML debugging patterns more accessible. Colleagues rely on her knack for turning nuanced algorithmic issues into practical, example-driven guidance that speeds onboarding and troubleshooting in production ML.
9 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (PhD) Physics, Doctor of Philosophy (PhD) Physics at The University of Texas at DallasComputer Science, Computer Science at Dallas College
Korean, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (14)
xgboost10
machine-learning10
amazon-sagemaker10
pytorch10
api10
tensorflow10
aws10
python10
sagemaker10
data-science9
documentation9
deeplearning-ai9
jupyter-notebook9
deep-learning9
Programming languages (3)
Github contributions (5)
aws/amazon-sagemaker-examples
Aug 2020 - Apr 2022
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:293 reviews, 33 commits, 87 PRs in 1 year 7 months
Contributions summary:Miyoung contributed to the development and debugging of machine learning models within the Amazon SageMaker examples repository. The contributions include publishing a notebook demonstrating how to build and debug custom training containers with Amazon SageMaker Debugger, addressing code differences, fixing typos, and incorporating feedback. The user also focused on making updates to the PyTorch, XGBoost, and TensorFlow examples, implementing debugging techniques.
pythonjupyter-notebooktrainingawssagemaker
aws/sagemaker-python-sdk
Oct 2020 - Dec 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:76 reviews, 30 commits, 34 PRs in 2 years 2 months
Contributions summary:Miyoung's commits focus on improving the documentation and adding examples for the SageMaker Debugger API, specifically related to debugger rules. They addressed code differences, improved docstrings, added examples, and fixed typos. Their work involved modifying the debugger's Python code and associated documentation files to enhance the user experience and clarify the API's usage.
pytorchsagemakerdeployingmxnetpython
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