Summary
Mark Hubenthal is a Senior Applied Scientist at Amazon with 11 years of research and industry experience translating inverse problems and PDE-constrained optimization into production-grade machine learning solutions. He blends deep mathematical training (PhD in Mathematics) and a track record of postdoctoral research on wave fields, radiative transfer, and imaging with hands-on applied science roles at Amazon since 2017. Known for tackling computationally intensive problems, he has bridged academic rigor and engineering at scale—authoring patents and consulting on ML, big data, and parallel computing early in his career. Based in Seattle, he brings expertise in numerical methods, optimization, and scientific computing to applied ML challenges that require both theory and pragmatic system design. A less obvious strength is his long history of teaching and mentoring, which informs clear communication of complex technical ideas across teams.
11 years of coding experience
9 years of employment as a software developer
PhD, Mathematics, PhD, Mathematics at University of Washington
Bachelors, Mathematics, Physics, Bachelors, Mathematics, Physics at Whitman College
Chinese, Spanish, English