Katerina Zanos is a Principal Machine Learning Engineer at The Walt Disney Company, building AI driven systems that shape how people discover and experience media at scale. Previously, she was a Senior Machine Learning Engineer at Meta, where she helped develop recommender models for Facebook Home Feed and Reels, focusing on deep learning architectures (including two tower networks) and approaches that improve relevance while giving new content its moment.
Before Meta, Katerina spent over five years at The New York Times, leading work on personalisation, newsroom messaging and audience growth tooling across search and social.
Upcoming Talks
12 May 2026
This session follows how Katerina is leading the shift from rule based curation to a machine learning driven recommendation platform across the home feed and vertical video feed. She shares how the team moved out of a limbo state, shipped simple ranking models fast, and used metrics and offline LLM support to guide smarter long term platform bets.
Outcomes:
- Identify when rule based personalization has peaked and where to introduce recommendations first
- Design an incremental ML roadmap that delivers quick wins while a shared platform is still being built
- Balance heuristics, classic ML and LLM assisted training to improve feeds without bloating production
- Align engineers, product and leadership around KPIs so experiments, platform work and delivery stay in sync