Summary
Paul Gustafson is a software engineer with 10 years of experience, currently focused on performance engineering for Meta’s recommender system ML infrastructure. He combines a PhD-level mathematical background with quantitative finance experience to tune large-scale systems where statistical rigor meets production constraints. His prior roles span medium-frequency alpha research, formal methods for robot behavior, and category-theory–informed RL research, reflecting a rare blend of theory and applied engineering. Based in Menlo Park, he brings deep expertise in tail-risk thinking and hedging—an uncommon lens for systems performance and reliability. Known for translating abstract math into robust, measurable system improvements, he excels at making high-dimensional models run reliably in real-world environments.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Mathematics, Doctor of Philosophy - PhD, Mathematics at Texas A&M University
Mathematics, Mathematics at Princeton University