Mallori Harrell is a founder and machine learning practitioner with a decade of experience building production data pipelines and applied ML across travel, healthcare, and aerospace. She has driven measurable impact—from cutting model pipeline runtimes by 83% at Rent the Runway to automating document and email parsing in the popular Unstructured open-source library used for ML preprocessing. Her background spans hands-on engineering (Python, Airflow, dbt, Spark) and research-driven analytics in cognitive neuroscience, giving her a strong bridge between signal-rich data and deployable systems. Now leading mission-driven travel ventures, she combines technical rigor with product-minded event design to create intentional offsites and faith-forward retreats that prioritize culture and purposeful experiences. Colleagues describe her as someone who moves fluidly between deep technical refactors and high-touch customer experiences—an unusual mix that fuels both robust systems and memorable human-centered programs.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Science (BS) Engineering Science, Bachelor of Science (BS) Engineering Science at Smith College
Master of Science (M.Sc) Cognitive Neuroscience, Master of Science (M.Sc) Cognitive Neuroscience at Università di Trento
Master’s Degree Data Science, Master’s Degree Data Science at Galvanize - San Francisco, SoMa
Certification Machine Learning Engineering, Certification Machine Learning Engineering at FourthBrain
Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.
Role in this project:
Backend Developer
Contributions:26 reviews, 13 commits, 25 PRs in 1 month
Contributions summary:Mallori primarily contributed to the backend functionality of the `unstructured-io/unstructured` repository. Their work involved refactoring and removing code related to PDF parsing, adding patterns for bulleted lists, and introducing new data structures like `EmailElement` and functions to extract attachment information. Furthermore, they expanded the functionality of the email partitioning, including the ability to parse email headers and embed images within email text.
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