Daniel Svonava is an entrepreneurial technologist whose 20-year career started early, with competitive programming and web development in high school, followed by algorithm research and internships at Google and IBM Research during university. His first entrepreneurial step was a computational photography startup, an experience that set the pattern for building at the edge of what machine learning can do in production.

Daniel then spent six years as a tech lead for ML infrastructure at YouTube Ads, where his ad performance forecasting engine powers the purchase of $10B of ads per year. That tenure gave him deep experience in the less glamorous but critical parts of machine learning: pipeline reliability, configuration, deployment trade-offs, and evaluation at massive scale.

Today Daniel is a co-founder of Superlinked, an ML infrastructure startup that makes it easier to build information-retrieval-heavy systems, from recommender engines to enterprise-focused LLM applications. He works on vector embeddings, hybrid search, and the infrastructure decisions behind modern search and retrieval, and shares this thinking through Superlinked’s VectorHub.