Leona Cook

Portland, Oregon, United States

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Summary

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Rockstar

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Top School

Leona Cook is a documentation and developer-experience specialist with 13 years of experience crafting developer-facing docs, APIs, and publishing pipelines for complex C++, Python, and mixed-language projects. She contributed technical writing and API design for Intel’s AI efforts (nGraph) and other open-source projects, building Sphinx/Doxygen pipelines and custom themes that improve discoverability and accessibility. Comfortable reviewing code via git workflows and translating developer support feedback into actionable documentation, she pairs engineering empathy with a strategist’s view of the documentation lifecycle. Based in Portland, she also brings hands-on product and support experience from startups to enterprises, including fraud analysis and high-volume customer support, which informs pragmatic, user-centered docs and training. As a Native American who donates expertise to indigenous causes, she prioritizes ethically aligned collaborations and may decline work from organizations that exploit marginalized communities.

13 years of coding experience

Anthropology Japanese, Anthropology Japanese at Pacific University

Master of Business Administration Economics, Master of Business Administration Economics at Southern Utah University

Github Skills (6)

api-documentation10

rs10

restructuredtext10

sphinx10

documentation10

technical-writing10

Programming languages (5)

C++GoHTMLRubyPython

Github contributions (5)

NervanaSystems/ngraph

Jan 2018 - May 2020

nGraph - open source C++ library, compiler and runtime for Deep Learning

Role in this project:

Technical Writer

Contributions:318 commits, 146 PRs, 677 pushes in 2 years 4 months

Contributions summary:Leona's contributions primarily revolve around enhancing the documentation for the nGraph library. They added examples, provided clear descriptions of operations, and added a glossary of terms, improving the accessibility and understanding of the library's functionality. They also contributed to the structure and content organization, updating illustrations and ensuring a well-defined structure for the documentation.

inference-enginecppc-librarydeep-learningtvm

indie/qmec

Sep 2020 - Jul 2022

Contributions:97 commits, 92 pushes, 2 branches in 1 year 10 months

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