Samuel Taylor is a Staff Machine Learning Engineer based in Austin with 12 years of experience turning research-grade ideas into production ML systems that drive business impact. He’s comfortable across the ML stack—feature stores, Kubeflow, TensorFlow, CatBoost, FastAPI and GKE—and has shipped high-impact projects like a 29% conversion uplift from lead-scoring and scalable lookalike/retrieval systems. Previously he built large-scale data and serving pipelines at Indeed and helped operationalize models and ETL at startups and enterprises alike. Samuel mentors others, from students learning to code to cross-functional teams, and brings a pragmatic focus on trustworthy models and imbalanced-data challenges. He blends hands-on engineering with a storyteller’s touch—publishing a pun generator and speaking at major data-science conferences—making complex systems both reliable and approachable.
12 years of coding experience
8 years of employment as a software developer
Bachelor of Science (B.S.), Computer Science, 4.00, Bachelor of Science (B.S.), Computer Science, 4.00 at Baylor University
Contributions:30 commits, 2 PRs, 23 pushes in 3 years 5 months
puns
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Samuel Taylor - Staff Machine Learning Engineer at Coinbase