Summary
Jes Ford is a Staff Machine Learning Engineer with 11 years of experience applying ML to product-focused problems, currently leading modeling efforts at Block. With a PhD in Physics and a background in astrophysics research, Jes blends rigorous probabilistic modeling and uncertainty quantification with pragmatic productionization—previously shipping NLP systems for Cash App and deep-learning pipelines for drug discovery at Recursion. They have a track record of introducing reproducible software practices, building cloud-backed model registries, and improving data quality to enable cross-team model comparisons. A regular conference speaker and open-source author, Jes is as comfortable presenting at PyCon as designing active learning strategies or Bayesian MCMC workflows. Based in South Lake Tahoe, they bring a scientist’s curiosity to product engineering, often surfacing interpretable model insights (saliency, GradCAM) that bridge ML and domain stakeholders.
11 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Physics & Astronomy, Doctor of Philosophy (Ph.D.), Physics & Astronomy at The University of British Columbia
Bachelor of Science (B.Sc.), Physics (Math minor), Bachelor of Science (B.Sc.), Physics (Math minor) at University of Nevada, Reno