Summary
Enrique Nueve is a machine learning engineer and researcher with eight years of experience applying statistics and deep learning to real-world systems, currently an ML Intern at Apple after multi-year data science roles at the Federal Reserve Bank of Chicago. He specializes in R&D for edge AI—developing, deploying, and optimizing models for on-device inference—and has published IEEE work on nowcasting net radiation from his Argonne National Laboratory research. Comfortable across the stack, Enrique brings Python-centered ML engineering plus data engineering experience with MySQL, Apache Airflow, and languages including R, C++, and Go. His background in rigorous statistics (B.S. in Statistics, strong graduate coursework) complements practical skills in NLP, computer vision, time-series forecasting, and concept-drift/active learning for online systems. He thrives in interdisciplinary Agile teams that mix researchers, developers, electrical engineers, and DevOps, and is noted for completing graduate-level courses while maintaining a 3.92 GPA. Notably, his work on edge caching/offloading and nowcasting shows a rare blend of environmental science application and production-focused ML engineering.
8 years of coding experience
6 years of employment as a software developer
Associate of Science - AS, Associate of Science - AS at Elgin Community College
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of Colorado Boulder
Bachelor's degree, Statistics, Bachelor's degree, Statistics at Northern Illinois University