Michael Mccabe is a Senior Research Scientist with 11 years of experience applying machine learning and computational science across healthcare, finance, and physical systems. He has led projects ranging from TB-scale dataset curation and foundation models for physical dynamics to lightweight models optimized for low-powered devices, giving him rare hands-on expertise across both massive and constrained compute regimes. At Polymathic AI and the Flatiron Institute he helped build a 1.3B-transformer (Walrus) and a 15TB dataset (the Well), collaborating in large cross-disciplinary teams on diffusion, multi-modal modeling, and model efficiency. His background blends rigorous academic work (PhD/MS in CS/Applied Math) with production-focused roles at Google, national labs, and industry, enabling him to translate numerical analysis insights into practical deep-learning architectures. He is based in the San Francisco Bay Area and is comfortable scaling systems from single-device deployments to hundreds of GPUs while probing model limitations through careful analysis.
11 years of coding experience
11 years of employment as a software developer
BA, Economics, International Studies, BA, Economics, International Studies at Northwestern University
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Colorado Boulder
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