Summary
John Reid is a research engineer with 13 years of experience bridging academia and industry, currently driving research at DeepMind after leading AutoML and document-understanding teams at Blue Prism. He combines deep learning, classical ML and Bayesian statistics to take novel models from concept to production, with a particular interest in hybrids that marry Bayesian rigour with neural representational power. His background spans systems biology and neuroimaging—where he supervised generative-model work at the Alan Turing Institute and applied Bayesian methods in the Gurdon Institute—giving him rare domain fluency across biotech, healthcare and financial services. A seasoned team lead and mentor, he has hands-on experience shipping ML products in commercial settings and publishing research in collaborative environments. Notably, his career began in high-performance middleware and quantitative engineering, which informs a pragmatic, performance-conscious approach to productionising complex models.
13 years of coding experience
21 years of employment as a software developer
Bachelor of Arts, Mathematics, Bachelor of Arts, Mathematics at University of Cambridge
11 'O'-levels and 4 'A'-levels, 11 'O'-levels and 4 'A'-levels at King Edward's School Bath