Evan Curtin is a Principal Applied Scientist in Chicago with nine years of experience building ML and LLM systems that operate in high-stakes, production environments. He designs models, evaluation frameworks, and deployment pipelines together so teams can iterate rapidly and ship tools that deliver measurable impact—most notably leading Relativity’s LLM-based document classification used in active eDiscovery litigations. Evan pairs a strong research background in computational chemistry and high-performance C++ with hands-on engineering skills (Kubernetes, Argo, Spark, AWS) to diagnose unexpected production behavior and unblock stalled projects. He’s known for privacy-preserving evaluation pipelines and reliability metrics for LLMs, and for translating vague goals into concrete, testable systems that users rely on. Colleagues lean on him for pragmatic solutions that balance experimental agility with operational safety.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Science (BSc) Chemistry, Bachelor of Science (BSc) Chemistry at Drexel University
Doctor of Philosophy (Ph.D.) Candidate Unfinished Physical Chemistry, Doctor of Philosophy (Ph.D.) Candidate Unfinished Physical Chemistry at University of Illinois Urbana-Champaign
Auto-generated API documentation for python and mkdocs
Contributions:13 releases, 6 commits, 40 pushes in 8 months
mkdocsapipythondocstringsdocstring
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Evan Curtin - Principal Applied Scientist at Relativity