Kevin Loftis
Software Engineer, Async Marketplace at Whatnot
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolKevin Loftis is a software engineer with 11 years of experience building production ML and data infrastructure, currently driving Whatnot’s Async Marketplace efforts after leading high-impact data platforms at Reddit. He designs and ships Kubernetes-native, streaming-first systems—authoring CDC pipelines, Flink-based event distributors, and a feature store SDK—focused on scalability, low-latency inference, and cost-efficient Kafka operations. His contributions to the open-source Feast feature store showcase practical BigQuery integration and robust Python SDK work that bridge ML tooling and production needs. At Whatnot he has taken multiple products 0→1, driving measurable growth (e.g., 5x GMV and comment engagement) while also engineering analytics and training dataset frameworks that accelerate ML iteration. Based in San Francisco, he blends a machine-learning research background with hands-on backend and infrastructure engineering to move models from experiment to reliable production.
11 years of coding experience
6 years of employment as a software developer
Master's degree, Data Science, Master's degree, Data Science at University of San FranciscoPost-Bac studies, Computer Science, Post-Bac studies, Computer Science at Portland State UniversityBachelor of Science (BS), Molecular Biology, Bachelor of Science (BS), Molecular Biology at Lipscomb University
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
feature-store10
bigquery10
python10
data-engineering10
data-science9
mlops9
documentation8
Programming languages (8)
Github contributions (5)
feast-dev/feast
Oct 2021 - Jan 2023
The Open Source Feature Store for AI/ML
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 review, 3 commits, 6 PRs in 1 year 3 months
Contributions summary:Kevin contributed to the Feast feature store project by implementing and improving features related to BigQuery integration. Their work included adding functionality to the BigQuery offline store configuration, fixing docstring typos, and enhancing the handling of Stream Feature Views. The user also updated the codebase to include new types and to make existing features more robust. Overall, the user's contributions focused on improving the functionality and documentation within the Python SDK.
pythondata-qualitydata-sciencemlmachine-learning
MSDS698/2020-product-analytics-group-project-impulses
May 2020 - Jul 2021
2020-product-analytics-group-project-impulses created by GitHub Classroom
Contributions:4 PRs, 68 pushes, 25 branches in 1 year 1 month
reactanalyticsgithub-classroomproduct-analyticsjavascript
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