Summary
Leo Horwitz is a Machine Learning Engineer with 11 years of experience building production ML systems that prioritize both speed and evaluation rigor. Based in Denver, he has shipped ultra-low-latency fraud detection (sub-100ms) and cut inference runtimes by 80%+ through pipeline and model optimization at Ibotta. He combines practical MLOps—Docker, SageMaker, Spark, MLflow, Databricks, and AWS—with principled model evaluation using ROC/PR analysis and benchmarking to align metrics with business goals. Prior roles span full-stack and systems work, from deploying sentence-transformer models to improving data quality pipelines that routed hundreds of thousands of records for review. Outside production systems, he’s led an AI student org curriculum and applied RL to novel problems, signaling a mix of engineering rigor and hands-on research curiosity.
11 years of coding experience
1 year of employment as a software developer
High School Diploma, 4.47, High School Diploma, 4.47 at H-B Woodlawn Secondary Program
California Polytechnic State University, San Luis Obispo
4.43, 4.43 at Taipei American School