Nathaniel Diamant is a machine learning engineer and researcher with a decade of experience building production ML systems and applied AI in biotech and academia. He currently splits his time between senior AI/ML roles at Genentech and graduate research in computer science at Stanford, bringing both industry-grade engineering and research rigor to large-scale biological problems. His background spans the full modeling pipeline—from C++ data backends and SQLite to Python modeling, orchestration with Luigi/AWS, and visualization—developed across organizations like Broad Institute, Yelp, and AdRoll. A Harvey Mudd-trained mathematician and programmer with top grades and early work on Markov models of student learning, he combines strong quantitative foundations with practical experience shipping reliable, tested pipelines that process thousands of daily items. Not obviously, Nathaniel’s career blends hands-on engineering in production systems with active research pursuits, positioning him to bridge experimental methods and deployable ML in regulated environments.
10 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, Math and Computer Science, 3.884 GPA, Bachelor’s Degree, Math and Computer Science, 3.884 GPA at Harvey Mudd College
Mathematics, 4.00, Mathematics, 4.00 at Berkeley City College
High School, 3.97 unweighted GPA, High School, 3.97 unweighted GPA at Berkeley High School
Contributions:14 pushes, 1 branch in 3 years 3 months
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