Kevin Loftis
Software Engineer at Whatnot
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolKevin Loftis is a software engineer with a strong machine learning and data science background, specializing in building high-performance production ML systems. As a founding engineer of Reddit’s Machine Learning Features team, he led the creation of a real-time streaming feature platform built on Flink, KSQL, Kafka, Redis, BigQuery, and Kubernetes, and designed the Feature Store API and a Python SDK. He is currently applying his expertise at Whatnot in San Francisco, driving scalable ML deployments and data-intensive engineering. An active open-source contributor, he has significantly contributed to Feast, implementing BigQuery integration, offline store configuration, and Stream Feature Views, and enhancing the Python SDK and docs. His career spans research and industry roles, blending ML research with production infrastructure, with a focus on MLOps and streaming systems.
11 years of coding experience
6 years of employment as a software developer
Post-Bac studies, Computer Science, Post-Bac studies, Computer Science at Portland State UniversityMaster's degree, Data Science, Master's degree, Data Science at University of San FranciscoBachelor of Science (BS), Molecular Biology, Bachelor of Science (BS), Molecular Biology at Lipscomb University
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
feature-store10
bigquery10
python10
data-engineering10
data-science9
mlops9
documentations8
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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