Summary
Matthew Robinson is a Technical Lead and data scientist with nine years building cloud-native, AI-driven systems that bridge infrastructure, embedded/HPC, and domain-specific model evaluation. He designs and ships production-grade platforms—from a SOC2-ready enterprise AI training workspace to KubeBench, an open benchmark and live service that improved Kubernetes YAML validity by ~20%—and has repeatedly turned research into revenue-positive launches on short timelines. A UC Berkeley MIS/Stats graduate, he pairs hands-on engineering (Kubernetes, Terraform, FastAPI, GKE, GPU scheduling) with product instincts honed by founding startups and interviewing 100+ customers. His work at NASA, BASF, and in developer tooling reflects a focus on translating what data actually knows into tailored intelligences rather than generic models. Colleagues rely on him to deliver measurable operational gains and reproducible model lineage in regulated, production environments.
9 years of coding experience
7 years of employment as a software developer
Master of Information and Data Science, Applied Statistics, Master of Information and Data Science, Applied Statistics at University of California, Berkeley
Software Engineering Immersive Fellowship, Computer Software Engineering, Software Engineering Immersive Fellowship, Computer Software Engineering at General Assembly
Intensive Machine Learning Industry Specialization Course, Machine Learning Engineering, Intensive Machine Learning Industry Specialization Course, Machine Learning Engineering at FourthBrain
Bachelors, Arts, Entertainment, and Media Management, Bachelors, Arts, Entertainment, and Media Management at Columbia College Chicago
Portuguese, English, German