Company engineering blog
Creative engineers and data scientists building a world where you can belong anywhere. http://airbnb.io
10 posts in the last 90 days · newest 6 Oct
Zuofei Wang
How we capture real production database traffic at Airbnb and replay it offline to load-test, plan capacity, and de-risk upgrades. By: Zuofei Wang, Erluo Li Introduction At Airbnb, MySQL-compatible databases are a critical backbone of our online database infrastructure: a fleet…
Wei Jiang
How Airbnb uses proximity signals to personalize without relying on individual user history. By: Wei Jiang, Bin Xu, Bharathi Thangamani, Weiwei Guo, Sundar Srinivasavaradhan, Tracy Yu, Huiji Gao, Swapnil Ghike, Michael Kinoti Great personalization starts with knowing your user…
Pengyu Hou
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. By: Pengyu Hou, Yuli Han, Daochen Zha, Haozhen Ding, Xin Liu, Sophie Wang, Pallavi…
AirbnbEng
How Airbnb’s agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By: Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you’ll…
Adam Bloomston
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize The data in Project Lighthouse is powered by privacy-preserving anonymization code. We’ve put this code into open source, and published two new technical papers detailing the scalable algorithms and data…
Harrison Katz
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, bookings, cancellations, and a range of…
Jose Santos
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. By: Jose Santos, Mike Barry For Airbnb, logins at irregular intervals are…
Rohit Girme
How Airbnb teams build trustworthy Generative AI products by treating evaluation as a first-class engineering discipline; not an afterthought. By: Rohit Girme, Dan Miller, Mia Zhao, Lifan Yang, Clint Kelly Introduction Generative AI breaks a lot of the assumptions that used to…
Daochen Zha
How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Jun Shi, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Michael Kinoti, Stephanie…
Baharak Saberidokht
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. By: Baharak Saberidokht Introduction Shipping a production LLM system means iterating fast on improvements…
