Browse past weeks of engineering reads.
How to personalize Airbnb search results by modeling the multi-session guest journey to surface relevant listings at the right time rather than treating each search in isolation.
Reducing LLM evaluation iteration cycles from weeks to a day to enable fast experimentation on non-deterministic model improvements in production systems.
Reliably delivering configuration changes to thousands of Airbnb service instances in Kubernetes, with changes occurring multiple times per minute at scale.
Building reliable forecasting models for marketplace demand when historical data is unavailable or unreliable due to unprecedented market shocks.
Building a metrics storage system capable of ingesting 50 million samples per second while reliably storing 2.5 petabytes of time series data at scale.
Building forecasting models that remain accurate during sudden market shocks like a global pandemic, where historical data no longer predicts future outcomes.