Robert Friel is a machine learning engineer with nine years of experience who blends rigorous academic training (PhD-level applied math/atmosphere-ocean science) with hands-on product delivery in healthcare and ML startups. He has driven end-to-end ML systems—from contrastive and transfer-learning models to production pipelines and templating systems—demonstrating both research depth and pragmatic engineering across services and languages. At 98point6 he shipped clinician-facing features that improved efficiency and built automation to replace manual model training, and he continued to scale ML efforts at Galileo before joining Transluce. Comfortable stepping outside his comfort zone, he routinely implements integration and backend code in unfamiliar stacks to get features over the line. Based in Seattle, he pairs a researcher’s attention to model interpretability with a maker’s focus on operational reliability and user impact.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Atmosphere Ocean Science and Mathematics, Doctor of Philosophy (Ph.D.) Atmosphere Ocean Science and Mathematics at Courant Institute of Mathematical Sciences
Data Science Intensive, Data Science Intensive at Galvanize - Seattle, Pioneer Square
Bachelor of Arts (B.A.) Physics, Bachelor of Arts (B.A.) Physics at Reed College
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Robert Friel - Member Of Technical Staff at Transluce