Kevin Moore
Sr. Staff Applied Research Scientist at ServiceNow
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKevin Moore is a Sr. Staff Applied Research Scientist in Menlo Park with 11 years of experience building production-ready ML systems, from AutoML on Spark to LLM alignment and efficient inference. He has deep expertise in model interpretability, data lineage and drift detection from leadership roles at Salesforce and Meta, and was an early technical hire who architected retrieval, ranking, and fine-tuning workflows at Cuein AI. Kevin contributed to the widely used TransmogrifAI AutoML project, adding feature-distribution insights and text-length transformers that improved model diagnostics. His background includes a Ph.D. in Physics and postdoctoral computational modeling, which informs a rigorous, physics-style approach to reproducible pipelines and statistical validation. He combines research rigor with hands-on engineering, mentoring teams and shipping tools that make models more reliable in real-world settings. Notably, he translates complex academic modeling techniques into scalable production practices across diverse domains.
10 years of coding experience
16 years of employment as a software developer
Ph.D. Physics, Ph.D. Physics at UC Santa BarbaraBA Physics, BA Physics at Cornell University
Japanese
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
transformers10
apache-spark10
machine-learning10
feature-engineering10
automl10
scala10
testing9
Programming languages (2)
Github contributions (5)
salesforce/TransmogrifAI
Aug 2018 - Oct 2020
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 21 reviews, 44 commits in 2 years 1 month
Contributions summary:Kevin contributed significantly to the `salesforce/transmogrifai` repository, an AutoML library built on Apache Spark. Their commits focused on enhancing model insights, specifically by adding distribution calculations within the RawFeatureFilter and integrating them into ModelInsights. The user also added new functionality to the library by adding a Text Length calculation estimator/transformer. They also corrected issues in the evaluation of multiclass classification metrics.
pythonsalesforcetransformationstransmogrifyestimators
Jauntbox/SurgeTrends
Jan 2016 - Jan 2016
Contributions:9 commits, 8 pushes, 1 branch in 15 days
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