Will Kurt is an AI engineer and technical author with 17 years of experience building and shipping ML/AI products across startups and Fortune 500s, now focused on agentic systems, LLM inference optimization, and practical reliability. He co-designed the Coalescence inference algorithm at .txt—credited with 2–5× LLM speedups and noted by Andrej Karpathy—and most recently led agentic workflows and forecasting as Senior Principal Data Scientist at Gierd. Author of three technical books (including the widely read Bayesian Statistics the Fun Way) and creator of the decade-running Count Bayesie blog, he blends a rigorous Bayesian foundation with hands-on engineering. His background spans applied roles from pricing and recommendations to large-scale experimentation and production ML, and he’s built courses with DeepLearning.AI, showing a talent for turning statistical ideas into usable tools. Based in Seattle, he combines technical depth, clear communication, and a persistent curiosity for making LLM systems efficient and actually useful.
17 years of coding experience
16 years of employment as a software developer
Bachelor of Arts (BA), English Language and Literature, General, Bachelor of Arts (BA), English Language and Literature, General at Rutgers University
Master's degree, Computer Science, Master's degree, Computer Science at University of Nevada, Reno
Master's degree, Library and Information Science, Master's degree, Library and Information Science at Simmons University
--, Computer Science, --, Computer Science at Boston University
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