Summary
Charles Siegel is a Senior Applied Scientist with 11 years of experience applying a Ph.D. in mathematics to machine learning, distributed systems, and big data at organizations including Meta and Amazon. He specializes in scalable deep learning and large-scale training, bringing uncommon mathematical rigor to model design, optimization, and system-level performance. His background spans research roles at national labs and academia, where he extended frameworks like TensorFlow for scientific datasets and published work on efficient training techniques. At Cray and HPE he helped customers and internal teams get the most out of HPC for deep learning, and at Meta focused on internal ML engineering for production-scale systems. Now based in Seattle, he blends theoretical insight with hands-on engineering to move models from research to production across cloud and HPC environments. Outside of work he posts hobby projects on GitHub tied to games he plays, reflecting a practical curiosity that fuels his applied research.
11 years of coding experience
13 years of employment as a software developer
Bachelor of Science (BS) Mathematics, Bachelor of Science (BS) Mathematics at Rutgers University
Doctor of Philosophy (Ph.D.) Mathematics, Doctor of Philosophy (Ph.D.) Mathematics at University of Pennsylvania
English, French, Japanese, German, Spanish