Todd Young
Senior Software Engineer at BambooHR
Vineyard, Utah, United States
Summary
👤
Senior🎓
Top SchoolTodd Young is a Senior Software Engineer with over a decade of experience building Microsoft-stack solutions and leading development teams, most recently serving in senior engineering and leadership roles including Head of Development and Director of Software Engineering. He founded and ran a small software company, giving him end-to-end experience from scoping and bidding to architecture and delivery, and now contributes to ML tooling—helping refactor core components like gp_minimize in the scikit-optimize project. Based in Vineyard, Utah, he pairs hands-on engineering with operational leadership at BambooHR while also bringing unusual discipline and leadership from more than a decade in the Utah National Guard, including deployment to Afghanistan and qualification as an Apache helicopter pilot. Dependable and results-driven, he excels at translating customer needs into robust, maintainable systems and enjoys the puzzle-solving aspects of complex technical challenges.
10 years of coding experience
12 years of employment as a software developer
Associate of Science (AS), General Science, Associate of Science (AS), General Science at Utah Valley UniversityBachelor of Applied Science (BASc), Computer Science, Bachelor of Applied Science (BASc), Computer Science at Brigham Young University
Chinese, Spanish
Github Skills (10)
scikit-learn10
hyperparameter-optimization10
bayesian10
optimisation10
python10
optimization10
scikit10
numpy9
machine-learning8
testing8
Programming languages (11)
Github contributions (5)
scikit-optimize/scikit-optimize
Jul 2017 - Jul 2017
Sequential model-based optimization with a `scipy.optimize` interface
Role in this project:
ML Engineer
ML EngineerContributions:7 commits, 8 PRs, 42 comments in 16 days
Contributions summary:Todd primarily contributed to refactoring and enhancing the `gp_minimize` and `Optimizer` modules within the scikit-optimize library, which focuses on sequential model-based optimization. Their work involved improvements to dimension transformations, import management, and the introduction of numpy data types to support more flexible dimension definitions, improving the library's usability and robustness. They also added a `DeadlineCallback` for controlling optimization time.
pythonbayesoptscipymodel-based-optimizationbayesian-optimization
utkML/utkml_examples
Feb 2017 - Apr 2017
Contributions:6 commits, 5 pushes, 1 branch in 2 months
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