Cory Locklear
Senior Principal Software Engineer at Yahoo
Charlotte, North Carolina, United States
Summary
👤
Senior🎓
Top SchoolCory Locklear is a Senior Principal Software Engineer with 12+ years of experience building data platforms and ML deployment tooling, currently leading engineering efforts at Yahoo after senior roles at RVO Health and Red Ventures. He blends hands-on systems work in Scala, Golang, Python and Spark with strategic architecture leadership, having progressed from individual contributor to director and distinguished engineer roles. Cory has practical ML deployment expertise—contributing to the MLeap project to improve model serialization and Spark compatibility—bringing models reliably into production. Based in Charlotte, he excels at translating complex analytics needs into scalable, production-grade services for large enterprise partners. Less obvious: he pairs deep backend engineering with a consistent focus on democratizing data science across organizations, enabling teams to ship predictive insights faster.
12 years of coding experience
19 years of employment as a software developer
CSC Computer Science, CSC Computer Science at North Carolina State UniversityBachelor of Science Computer Science, Bachelor of Science Computer Science at The University of North Carolina at Pembroke
Stackoverflow
28reputation
166reached
1answer
1question
Github Skills (18)
spark10
data-pipelines10
machine-learning10
data-serialization10
serialization10
scala10
deserialization10
data-pipeline10
transformers9
playframework6
tensorflow6
scikit-learn6
angularjs6
scikit6
json6
Programming languages (11)
Github contributions (5)
combust/mleap
Oct 2017 - Oct 2018
MLeap: Deploy ML Pipelines to Production
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 7 PRs, 30 comments in 1 year
Contributions summary:Cory made several contributions focused on enhancing the MLeap library for deploying ML pipelines. They addressed issues related to serialization and deserialization of machine learning models and metadata, allowing for arbitrary metadata encoding within the bundle. They also added support for Spark 2.3, integrating the FeatureHasher transformer and OneHotEncoderModel, which demonstrates an understanding of and work on the core components of machine learning model deployment. These updates included adapting code for different Spark versions and expanding MLeap's compatibility with Spark MLlib functionality.
transformerspythonml-pipelinesmleapdata-science
clocklear/ElvUI_FriendlyCastTimes
Oct 2021 - Dec 2024
Converts cast times greater than 60 seconds into more human-friendly representation (mm:ss or hh:mm, depending on duration)
Contributions:11 releases, 2 PRs, 15 pushes in 3 years 2 months
representationhuman-friendlytimesgreater-than
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