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.
Building reliable forecasting models for marketplace demand when historical data is unavailable or unreliable due to unprecedented market shocks.
Building forecasting models that remain accurate during sudden market shocks like a global pandemic, where historical data no longer predicts future outcomes.
Airbnb needed to advance its AI, data science, and machine learning capabilities across multiple domains (NLP, optimization, measurement science) to improve its travel and living platform, requiring solutions to challenges in search ranking, recommendation, experimentation, and large-scale data processing.
Producing valid and realistic mock data for GraphQL testing and prototyping is tedious to write and maintain; existing approaches like random value generation and field-level stubbing lack domain context, resulting in unconvincing and brittle test data that doesn't scale across a large schema.
Airbnb needed to build robust data science and economic modeling capabilities to understand and optimize their two-sided marketplace dynamics for policy and business decisions.
Airbnb users in the early trip planning stage often lack a clear travel destination, making it difficult to provide relevant recommendations and convert exploratory browsing into bookings.