Senior Staff Software Engineer, Host Pricing & Settings

<div class="content-intro"><p><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.</span></p></div><h4><strong>The Community You Will Join:</strong></h4> <p>The Host Pricing &amp; Settings team builds the platform and tools that help hosts run their business — with pricing strategies informed by market intelligence, comparable listings, and demand signals. We partner with Search, Listings, Tax, and Payments to ensure our guidance is accurate, timely, and trusted.</p> <p>Behind every pricing recommendation is a sophisticated ML system undergoing a fundamental rearchitecture. Our north star: a serving infrastructure where training, inference, and evaluation are consistent by design — features from a centralized store, model composition in one place, and backfills available on demand so data scientists and MLEs&nbsp; can evaluate candidates in days, not weeks.</p> <h4><strong>The Difference You Will Make:</strong></h4> <p>As a senior technical individual contributor, you will own the technical strategy for the full Modeling → ML Serving → API interface across the Host Pricing org. Although you will be at one of our highest levels of seniority, all individual contributors at Airbnb are Software Engineers — you are expected to be hands-on and contribute code.</p> <ul> <li>Define the architecture and contracts governing how models move from development to production — feature store design, model schema management, online/offline inference consistency, and multi-version support.</li> <li>Lead the buildout of a unified serving stack that eliminates per-model one-off implementations and gives data scientists a turnkey path from training to production.</li> <li>Architect backfill and evaluation infrastructure so the modeling team can simulate production inference over historical data in days, not weeks.</li> <li>Establish domain contracts between Modeling and Serving so each team can move independently with clear, enforced interfaces.</li> </ul> <h4><strong>A Typical Day:</strong></h4> <ul> <li>Review and evolve the ML serving architecture — making tradeoff calls on feature pipeline design, model composition, and API interfaces.</li> <li>Write and review code for feature engineering jobs, feature store configurations, and serving service endpoints.</li> <li>Partner with Data Science, MLE, MLI and core Pricing &amp