Penn Jenks
Machine Learning Engineer at Scale AI
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolPenn Jenks is a Machine Learning Engineer based in San Francisco with a decade of experience applying deep learning and LLM techniques to production problems across startups and scale-ups. Currently at Scale AI, he has progressed through increasingly senior ML roles at Zesty.ai and SVXR, shipping models and systems in real-world settings like insurance risk and geospatial analytics. An active open-source contributor, he has improved the JAX-based equinox library by adding attention mechanisms, indexing for Sequential modules, and bug fixes to GroupNorm—demonstrating both research rigor and engineering polish. His background includes hands-on leadership and high-stakes decision-making from early roles as a volunteer fire department Student Chief and wilderness guide, reflecting calm operational leadership under pressure.
9 years of coding experience
6 years of employment as a software developer
St. Paul's SchoolBachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at Sewanee-The University of the South
Github Skills (6)
neural-network10
deeplearning-ai10
deep-learning10
jax10
python10
pytest8
Programming languages (6)
Github contributions (5)
patrick-kidger/equinox
Jul 2022 - Sep 2022
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 9 commits, 5 PRs in 1 month
Contributions summary:Penn focused on improving the `equinox` library, a JAX-based neural networks library. Their contributions include fixing bugs related to `GroupNorm` and related documentation, as well as adding new features such as making the `Sequential` module indexable and adding methods. The user also added attention mechanisms and associated tests.
pytorchdeep-learningneural-networkscallablejax
jenkspt/gpt-jax
Jan 2023 - Feb 2023
Contributions:54 commits, 48 pushes, 3 branches in 1 month
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