Trenton Osborn is a Machine Learning Engineer with 11 years of experience building production ML systems across Big Tech and startups, currently working on Modern Recommendation Systems at Meta in New York. He has deep hands-on expertise in training and inference frameworks for large models, having contributed to AXLearn and GenAI infrastructure at Apple, and earlier improved ML tooling and model management at Weights & Biases. His background spans applied algorithms and signal processing for health analytics at Verily/Google, real-time feature engineering, and distributed training pipelines for energy and mobility analytics as a consultant. Trenton’s strong mathematical training (including programs at HSE, Princeton, Cornell and a math degree from Baylor) complements his engineering practice, enabling him to translate advanced modeling ideas into reliable, scalable systems. A recurring theme in his career is bridging research and production: he partners with researchers to implement novel methods while hardening pipelines for real-world use.
11 years of coding experience
4 years of employment as a software developer
Mathematics, Mathematics at Baylor University
Math in Moscow, Math in Moscow at Higher School of Economics
Summer Math Institute, Summer Math Institute at Cornell University
Summer Program in Analysis and Geometry, Summer Program in Analysis and Geometry at Princeton University
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial
Trenton Osborn - Machine Learning Engineer at Meta