Summary
Matthew Wolff is a Machine Learning Engineer with a decade of experience building and productionizing scalable ML systems, currently focusing on ad response prediction at Amazon in New York. He has a strong full-stack ML engineering background—having moved offline batch recommenders to low-latency online ranking systems, optimized streaming services for 15x efficiency gains, and re-architected data lake infrastructure to save hundreds of compute hours monthly. Trained in computational biology (CMU) and computer science/genetics (UW–Madison), he brings a rare mix of bioinformatics pipeline experience and cloud-native ML deployment expertise across AWS services like SageMaker, CDK, Glue and Step Functions. Founder of UW–Madison’s Data Science Club and a former graduate teaching assistant, he pairs hands-on engineering with mentorship and an eye for production reliability. An Amazon developer who delights in squeezing latency and cost out of systems, he also quietly applies domain knowledge from genomics to complex data problems.
10 years of coding experience
3 years of employment as a software developer
Bachelor’s Degree Computer Science Genetics & Genomics, Bachelor’s Degree Computer Science Genetics & Genomics at University of Wisconsin-Madison
Master's degree Computational Biology, Master's degree Computational Biology at Carnegie Mellon University
English, python, Latin