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Airbnb
Airbnb is an online marketplace that lets people rent out their spare rooms or homes to travelers for short-term stays.
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Latest articles
Airbnb ·
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize
Airbnb has open-sourced project-lighthouse-anonymize, a Python library powering their privacy-preserving anonymization used to measure user experience disparities. The release is accompanied by technical papers detailing Core Mondrian, an extended partition-based anonymization algorithm, alongside a framework for measuring anonymized data quality. The library enforces k-anonymity and p-sensitive k-anonymity while offering built-in data quality metrics.
Adam BloomstonAirbnb ·
How we knew COVID was over (and what our models had to unlearn)
Airbnb's forecasting team outlines their framework for managing production models that experience drift or external shocks. Rather than treating model updates as a generic retraining task, they distinguish between refitting with new data, respecifying the underlying model structure, and intentionally holding without changes. This discipline prevents teams from chasing temporary noise, carrying obsolete crisis assumptions, or over-engineering solutions during disruptions.
Harrison KatzAirbnb ·
Flexible Authentication: Reimagining authentication for millions of users at Airbnb
Airbnb overhauled its login and registration architecture by transitioning to a server-driven framework called Flexible Authentication. The new system separates user identification from verification, using a backend policy engine to dynamically serve the optimal auth challenge and fallback options based on regional and user context. This shift eliminated dead ends, reduced client bundle sizes, and dramatically sped up experimentation velocity across Web, iOS, and Android.
Jose SantosAirbnb ·
Eval-driven development: Lessons from evaluating GenAI at scale
Airbnb outlines its framework for eval-driven development (EDD), treating GenAI evaluation as a continuous engineering discipline rather than an afterthought. The strategy combines programmatic checks, calibrated LLM-as-a-judge evaluators, and human review to detect subtle quality regressions. By inspecting intermediate agentic execution traces and real-world failure modes, teams ensure generative models remain reliable and aligned with product requirements.
Rohit GirmeAirbnb ·
Personalizing Airbnb search by learning from the guest journey
Airbnb replaced hundreds of hand-crafted ranking features with a Transformer-based sequence model that captures both long-term booking history and short-term browsing behavior. To keep latency low and throughput high, sequence representations are generated via daily batch jobs and combined with real-time queries using a co-trained setwise ranker.
Daochen ZhaAirbnb ·
From weeks to a day: how we made LLM evaluation fast enough to iterate on
Airbnb built a four-layer LLM infrastructure framework to reduce iteration and evaluation turnaround from weeks to a single day. By using per-sample caching for generated references and judge scores, they established a deterministic evaluation foundation that separates genuine model drift from measurement noise. This setup enables rapid, bounded model hotfixes using micro LoRA adapters and comprehensive end-to-end validation across component boundaries.
Baharak SaberidokhtAirbnb ·
Scaling beyond one: How Airbnb evolved its data architecture for a multi-product world
To support its expansion from Homes into Experiences and Services, Airbnb evolved its offline data warehouse architecture to balance domain-specific needs with organizational consistency. Rather than enforcing a single global pattern, data teams adopted a framework of foundational principles that paired separate data models for unique product features with monolithic models for cross-cutting domains like payments and messaging.
Patrick LamAirbnb ·
Sitar-agent: Building a reliable dynamic configuration sidecar at scale
Airbnb modernized sitar-agent, a Kubernetes sidecar that delivers dynamic configuration updates to thousands of polyglot service instances without requiring redeployments. The architecture uses periodic AWS S3 snapshots to enable fast, decoupled pod startups and maintains local file-based storage for the main container to read configs with in-memory caching. Key design decisions include retaining an isolated sidecar over an in-process library and optimizing pull-based polling with server-side caching.
Bo TengAirbnb ·
When history fails you, borrow from geography
When historical travel data became uninformative during the asynchronous post-COVID recovery, Airbnb redesigned its demand forecasting to borrow signals across geographies rather than waiting for local data. Using a hierarchical Bayesian framework, the team propagated updated posterior estimates from early-recovering corridors as informative priors for structurally similar, later-recovering corridors. This allowed accurate corridor-level demand forecasting in near real time during periods of high disruption and uneven market reopening.
Harrison KatzAirbnb ·
Scaling Airbnb’s identity graph with a unified knowledge graph infrastructure
Airbnb migrated its massive identity graph from a third-party graph database vendor to a unified, in-house knowledge graph platform to solve long-tail latency and scaling bottlenecks. Built on JanusGraph with AWS DynamoDB for persistence and OpenSearch for indexing, the new architecture decouples storage operations from graph traversal logic. The migration improved P99 query latency, eliminated routine instance reboots, and supported a tenfold increase in write throughput.
Lucen Zhao