Max Bertolero
Principal Generative AI Engineer
Philadelphia, Pennsylvania, United States
Summary
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Senior🎓
Top SchoolMax Bertolero is a Principal Generative AI Engineer with 11 years of experience at the intersection of neuroscience and scalable AI, currently architecting multi-agent LLM systems and RAG pipelines to transform Medical Affairs, R&D, Commercial, and IT workflows at Madrigal Pharmaceuticals. He combines a PhD in Systems Neuroscience with deep ML/MLops expertise—building clinical-grade, explainable models and Azure-based AI infrastructure that power real-world evidence extraction and HEOR analytics. Previously he led science and ML teams for neuromodulation and FDA-cleared medical devices at Turing Medical, translating neuroimaging and clinical trial data into patient-subtyping diagnostics and production-ready ML products. Known for turning complex clinical questions into operational AI tools, he emphasizes secure, scalable deployments and clinician-in-the-loop validation. Based in Philadelphia, he brings a rare blend of domain knowledge, academic rigor, and hands-on engineering to drive impact where medicine meets generative AI.
11 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy - PhD, Systems Neuroscience, 4.0, Doctor of Philosophy - PhD, Systems Neuroscience, 4.0 at University of California, BerkeleyBachelor of Arts - BA, Valedictorian, PSYCHOLOGY, 4.2, Bachelor of Arts - BA, Valedictorian, PSYCHOLOGY, 4.2 at Columbia University in the City of New York
Stackoverflow
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0reached
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Github Skills (74)
feature-extraction10
cdr10
mod-manager10
post-processing10
asl10
journal9
electrophysiology9
toolbox9
graph-algorithms9
topological-data-analysis9
connectivity9
twitter-api9
mri8
location8
diffusion-mri8
Programming languages (8)
Github contributions (5)
PennLINC/PennLINC-Kit
Jan 2021 - May 2022
Common analysis functions used by the PennLINC scientists
Contributions:75 commits, 81 pushes, 2 branches in 1 year 4 months
scientists
mb3152/balanced_citer
Jun 2020 - Dec 2021
This python script guesses the race and gender (probabilistically) of the first and last authors for papers in your citation list (in the form of a .bib file) and compares your list to expected distributions based on a model that accounts for paper characteristics (e.g., author location, author seniority, journal, et cetera.) unrelated to race and gender.
Contributions:20 commits, 1 PR, 64 pushes in 1 year 6 months
citationdistributionspythonjournalbib
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