Jay Roberts
Member Of Engineering at poolside
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolJay Roberts is an AI-focused engineer with a Ph.D. in Mathematics and eight years of experience building and scaling research-driven ML systems across academia, government labs, and industry. He blends deep mathematical theory with practical engineering—developing privacy guarantees for LLMs, new loss functions that cut training time by over 40%, and large-scale distributed training and deployment at Amazon and MIT Lincoln Laboratory. Currently a Member of Engineering at poolside, he focuses on core evaluations of AI models while carrying a track record of translating theoretical results to product and customer-facing explanations. His background includes novel contributions to explainable AI, uncertainty-aware training, and seismic inversion using stochastic dynamical systems, reflecting a strong multidisciplinary bent. Based in Seattle, Jay brings rare comfort moving from formal proofs to multi-GPU production fine-tuning and stakeholder-facing presentations. An early-career pattern is his ability to shorten iteration cycles without sacrificing model guarantees—turning mathematical insights into measurable engineering wins.
9 years of coding experience
4 years of employment as a software developer
Ph.D Mathematics, Ph.D Mathematics at UC Santa Barbara
Spanish
Github Skills (16)
pytorch-lightning3
yaml-configuration3
neural-network3
reproducible-science3
yaml2
dataclasses2
scalable2
machine-learning2
deep-learning2
pytorch2
optuna1
crossword1
tensorflow1
robustness1
optimization1
Programming languages (2)
Github contributions (5)
KyleMylonakis/GeoSeg
Aug 2018 - May 2019
Implementations of various neural network architectures to infer the interface depth of simulations of the elastic wave equation with the frozen gaussian approximation.
Contributions:116 commits, 4 PRs, 44 pushes in 8 months
neural-network
Jay-Roberts/Screen_Shot_Classifier
Aug 2017 - Mar 2018
Contributions:94 pushes, 8 branches in 6 months
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