I am a fundamentals-driven developer with a background spanning data annotation and residential construction. I chose to learn computing from the ground up, starting with C and Go before Python, and I bring that same methodical, full-scope approach to every project.
I am eager to work in or alongside AI, especially in roles that leverage intelligent tooling to help ship better software faster. I have experience evaluating AI model outputs for accuracy, identifying data quality issues, and providing detailed feedback to improve training data quality.
I also have hands-on experience as a builder’s assistant, where I quickly adapted to new tasks, handled construction equipment and materials, and monitored supply and purchasing needs. This experience strengthened my attention to detail and ability to work responsibly in practical environments.
My technical interests include C, Go, SQL, HTML, Linux, Git/GitHub, shell scripting, and technical documentation. I am currently continuing my computer science education and building projects that combine software development with research and tooling.
I am pursuing a Bachelor’s degree in Computer Science and have completed CS50x: Introduction to Computer Science from Harvard University. I value continuous learning and enjoy working on projects that require both technical precision and thoughtful problem-solving.
One of my projects is Passage, a linguistic research tool for Scottish Gaelic, built with Go, HTML, and HTMX. I am looking for opportunities where I can contribute as a developer, especially in environments that value AI, data quality, and practical software delivery.
GPA: 4.47. Honor roll all high school semesters.
Certificate completed.
Ongoing degree program.
Quickly adapt to and learn new tasks as needed. Handle construction equipment and materials. Monitor supply and purchasing needs.
Evaluated AI model outputs for accuracy and adherence to task-specific rubrics. Identified and flagged data quality issues including mislabeled entries and edge cases. Provided detailed feedback on model performance to improve training data quality. Correctly and safely handled software and PII.
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