Summary
Daniel Campos is a CEO and former research scientist with 11 years of experience building and scaling ML-driven search, retrieval, and NLP systems across academia and industry. He earned a PhD in Computer Science from UIUC and combines deep research in IR/IE/NLP with product and engineering rigor developed at Microsoft, Snowflake, Neeva, and Neural Magic. His work includes practical advances such as CAPOT and KALE for efficient retrieval and embedding alignment, shipping hundreds of models for CPU-friendly inference, and building enterprise-grade unstructured data tooling. Comfortable moving between code, metrics, and go-to-market strategy, he has delivered systems that cut costs and latency while improving accuracy at scale. A former PM who turned researcher-founder, he pairs an architect’s eye for aesthetics and design with a rigorous, experimental approach to model efficiency. Based in New York, he’s equally at home prototyping novel algorithms and leading teams to productionize them.
11 years of coding experience
11 years of employment as a software developer
Master's degree, Computational Linguistics, Master's degree, Computational Linguistics at University of Washington
Bachelor of Science (B.S.), Computer Science, Bachelor of Science (B.S.), Computer Science at Rensselaer Polytechnic Institute
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Illinois Urbana-Champaign
English, Spanish