Summary
Andrew Figpope is a Principal Machine Learning Engineer and systems architect with 12 years of experience building scalable, production ML and data platforms across biotech, media, and startup environments. Based in Los Angeles, he has a track record of operationalizing large-scale compute (Spark, PyTorch, AlphaFold2) and driving infrastructure maturity—moving teams to reproducible Pulumi IaC, FedRAMP-ready setups, and attested CI/CD for traceable provenance. He blends hands-on engineering (optimizing gVCF merging, high-throughput Mongo imports, and Kryo serialization improvements in the chill project) with executive leadership, having built and scaled data organizations and cut multimillion-dollar costs through model operationalization. His work emphasizes collaborative research tooling—polyglot Jupyter kernels, serverless dev environments, and low-code ML tools—that measurably speed discovery and cross-team model transfer. He pairs a Columbia Engineering CS foundation with a habit of turning complex scientific pipelines into auditable, efficient production systems that reduce runtime and friction for researchers.
13 years of coding experience
11 years of employment as a software developer
B.S., Computer Science, B.S., Computer Science at Columbia Engineering
English