Noah Luna
Sr. Programmer Writer Documentation Product Lead
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolNoah Luna is a Sr. Programmer Writer and Documentation Product Lead with six years of experience translating ML and AI systems into clear, production-ready documentation and examples. Based in the San Francisco Bay Area, he has guided docs strategy and engineering at Weights & Biases—leading a small team to rebuild the docs site, add Japanese documentation using generative AI, and introduce CI tests to keep docs deployable. Previously at AWS he authored and reviewed SageMaker developer guides and curated Jupyter notebook examples used by customers, contributing to the widely used amazon-sagemaker-examples repository. Trained in geophysics and seismology, he brings a research mindset to reproducibility and testing, pairing hands-on ML engineering with product-focused documentation leadership.
5 years of coding experience
5 years of employment as a software developer
Bachelor of Arts, Geophysics, Bachelor of Arts, Geophysics at University of California, BerkeleyLudwig Maximilian University of MunichMaster of Science - MS, Geophysics and Seismology, Master of Science - MS, Geophysics and Seismology at Technical University Munich
Github Skills (12)
keras10
amazon-sagemaker10
machine-learning10
deep-learning10
tensorflow10
python9
jupyter-notebook9
horovod8
aws8
inference7
mlops7
data-science6
Programming languages (3)
Github contributions (5)
aws/amazon-sagemaker-examples
Jun 2020 - Oct 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:123 reviews, 12 commits, 40 PRs in 2 years 3 months
Contributions summary:Noah primarily contributed to example notebooks demonstrating machine learning model building, training, and deployment using Amazon SageMaker. Their commits focused on updating instance types and fixing issues within the example notebooks. The user demonstrated expertise in TensorFlow and Keras, as well as the use of distributed training with Horovod. They also made updates to the inference recommender tool.
pythonjupyter-notebooktrainingawssagemaker
ngluna/amazon-sagemaker-examples
Jun 2020 - Mar 2022
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
Contributions:26 pushes, 10 branches in 1 year 9 months
sagemakeramazon-sagemakerdeep-learningreinforcement-learningamazon
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.