Matt Lanzi is a software engineer with 8 years' experience designing cloud-native backend and data platforms that power simulation, experimentation, and production AI/ML systems. He excels at translating product requirements into scalable architectures and efficient data pipelines, with a track record of cutting analytics storage and resource usage dramatically while accelerating failure diagnostics from hours to minutes. Comfortable at the intersection of engineering rigor and real-world impact, he prioritizes simple, maintainable systems, clear ownership, and documentation that teams can rely on. His recent work spans advanced driver-assistance tooling at Woven by Toyota and cross-system traceability and MLOps proof-of-concepts at Ford. Based in Michigan, he brings startup grit from earlier roles and a practical bent for making ML and analytics workflows operationally reliable. An uncommon strength is his focus on measurable operational wins—cost, latency, and memory reductions—rather than purely feature delivery.
8 years of coding experience
5 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Oakland University
Computer Science, Computer Science at Macomb Community College
Bachelor of Science (B.S.) Kinesiology and Exercise Science, Bachelor of Science (B.S.) Kinesiology and Exercise Science at Michigan State University
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