Airbnb · Engineering blog · 2.7 a month · 14 posts since Jun 4, 2026
Nov 3
We benchmarked a fine-tuned E2B model against frontier APIs on a real production classification task. It matched their quality in under 100ms, at about a hundredth of the cost.
Oct 13
How we addressed the cold-start problem for Airbnb’s natural language search by jointly generating synthetic training data, queries and relevance labels, from de-identified booking and search signals.
Oct 6
How we capture real production database traffic at Airbnb and replay it offline to load-test, plan capacity, and de-risk upgrades.
Sep 29
How Airbnb uses proximity signals to personalize without relying on individual user history.
Sep 23
How two new Chronon capabilities, Push Mode and NRT Model Transform, allows us to provide more relevant search results instantly as a guest explores, rather than waiting for the next batch run.
Sep 15
How Airbnb’s agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure.
Aug 25
Project Lighthouse, Part 3: Introducing project-lighthouse-anonymize The data in Project Lighthouse is powered by privacy-preserving anonymization code.
Aug 19
When we retrain, when we rebuild, and when we leave a model alone. By: Harrison Katz A forecast that carries weight The Forecasting Data Science team at Airbnb produces many of the forecasts the rest of the company plans around: demand...
Aug 12
Rebuilding login and signup surfaced product insights, not just technical challenges. Here’s how we designed Flexible Authentication at the intersection of product intuition and technical architecture.
Jul 28
How Airbnb teams build trustworthy Generative AI products by treating evaluation as a first-class engineering discipline; not an afterthought.
Jul 21
How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time.
Jul 14
Training an LLM is the easy part. The hard part is designing experiments and evaluations that you can trust enough to know whether the new model is actually an improvement.
Jun 9
How Airbnb’s data engineers and analytics engineers built a consistent and flexible data modeling framework to support the expansion into Homes, Experiences, and Services.
Jun 4
How Airbnb built a Kubernetes sidecar to deliver dynamic configuration reliably at scale. By: Bo Teng, Cosmo Qiu, Siyuan Zhou, Ankur Soni, Xin Huang, Willis Harvey Introduction In our previous post, we explored Airbnb’s dynamic...
Airbnb engineers and data scientists on search, payments, ML and infrastructure.
Machine learning (5), Research (2), AI and agents (2) in the last six months.
Posts hiddenSep 25
- to 2 posts: Airbnb moved its engineering blog from Medium to airbnb.tech; the Medium copy of a post airbnb.tech also carries is hidden
FeedSep 25
https://medium.com/feed/airbnb-engineering to https://airbnb.tech/feed/
Blog addressSep 25
https://medium.com/airbnb-engineering to https://airbnb.tech/
Official checkSep 25
not reviewed to official: airbnb.tech publication marked as Airbnb's own (links to airbnb.com terms and privacy)
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