Senior Machine Learning Research Engineer at Allen Institute for AI (AI2)
Old Toronto, Ontario, Canada
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Summary
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Jeremy Mcgibbon is a Senior Machine Learning Research Engineer with a PhD in Atmospheric Sciences and 11 years of experience applying ML to weather and climate systems. He has a track record of translating advanced research into practical models—most notably using neural networks to represent subgrid clouds and turbulence and cutting multi-day forecast error by over half in single-column tests. At AI2 and previously Vulcan, he blends deep learning engineering (Keras/TensorFlow, recurrent architectures) with domain expertise from observational validation and high-resolution simulation work. Based in Toronto, he writes and maintains Python atmospheric modeling code and has a background running models on supercomputers and handling messy observational datasets. Colleagues rely on him for bridging rigorous research, reproducible code, and operationally-relevant predictions.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Atmospheric Sciences and Meteorology, Doctor of Philosophy - PhD, Atmospheric Sciences and Meteorology at University of Washington
Honors Bachelor of Science, Physics, 3.85 CGPA, Honors Bachelor of Science, Physics, 3.85 CGPA at University of Toronto
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Jeremy Mcgibbon - Senior Machine Learning Research Engineer at Allen Institute for AI (AI2)