Ken Lee
AI Success Engineer at OpenAI
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolKen Lee is an AI Success Engineer with 5 years of hands-on experience helping organizations deploy and scale machine learning in production across healthcare, life sciences, and enterprise domains. He has moved from customer-facing data science and ML engineering roles at DataRobot and CognitiveScale to leading GenAI solutions and success engineering at Weights & Biases, Google, and now OpenAI, specializing in model registries, CI/CD, and MLOps. Ken blends deep technical chops—building end-to-end demos on Kubernetes, TorchServe, and GitHub Actions and contributing model-registry enhancements to popular open-source wandb examples—with a consultative approach to pre/post-sales strategy. Based in Austin, he pairs an MS in Computer Engineering with practical data engineering experience (including large-scale PySpark pipelines for public health) to translate research-grade models into reliable production services. Notably, his open-source work documents concrete patterns for model artifact management and versioning that teams can reuse to shorten production timelines.
5 years of coding experience
10 years of employment as a software developer
Master of Science - MS Computer Engineering, Master of Science - MS Computer Engineering at The University of Texas at Austin
Github Skills (6)
weighting10
machine-learning10
jupyter-notebook10
weight10
model-registry10
python9
Programming languages (2)
Github contributions (5)
wandb/examples
Aug 2022 - Sep 2022
Example deep learning projects that use wandb's features.
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 16 commits, 30 PRs in 1 month
Contributions summary:Ken's contributions center on enhancing the model registry functionality within the `wandb/examples` repository. The commit includes changes to a Jupyter Notebook demonstrating the model registry end-to-end. The user is adding model registration, showing how to log model artifacts, link them to registered models, and manage different model versions.
pytorchdeep-learningdeep-learning-examplemachine-learningwandb
kenleejr/llm-digest
Sep 2023 - May 2024
Contributions:7 pushes, 1 branch in 7 months
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