Senior Staff Software Engineer, Host Pricing & Settings

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. The Community You Will Join: The Host Pricing & 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. 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 can evaluate candidates in days, not weeks. The Difference You Will Make: 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. - 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. - 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. - Architect backfill and evaluation infrastructure so the modeling team can simulate production inference over historical data in days, not weeks. - Establish domain contracts between Modeling and Serving so each team can move independently with clear, enforced interfaces. A Typical Day: - Review and evolve the ML serving architecture — making tradeoff calls on feature pipeline design, model composition, and API interfaces. - Write and review code for feature engineering jobs, feature store configurations, and serving service endpoints. - Partner with Data Science, MLE, MLI and core Pricing & Availability systems BE teams to define artifact handoffs and integration contracts. - Drive milestone planning across the Host Pricing & Settings org, sequencing work to deliver value incrementally. - Mentor engineers through design reviews and hands-on pairing on the hardest infrastructure problems. Your Expertise: - 12+ years in backend or platform engineering, with substantial experience building production ML systems or data-intensive infrastructure. - Strong programming skills in Java, Kotlin, Scala, and/or Python. - Deep understanding of ML systems design: feature stores, training/serving consistency, model versioning, and online/offline inference pipelines. - Experience with high-scale batch and real-time data pipelines (Spark, Airflow, Kafka, or equivalent), including point-in-time correctness for backfills. - Expertise with architectural patterns of large, high-scale applications — well-designed APIs, efficient data contracts, multi-tenant serving infrastructure. - Proven ability to lead cross-team technical initiatives spanning ML and platform engineering. Preferred Qualifications: - Feature Store Depth: Production experience with Chronon, Tecton, Feast, or equivalent — including online/offline consistency and backfill automation. - Model Serving Infrastructure: Experience with model schema management, multi-version support, and model composition frameworks. - Domain Contract Design: Track record defining and enforcing technical contracts between ML modeling, MLI, serving teams and/or product surfaces. - Evaluation Velocity: Measurable impact improving the speed at which ML teams evaluate candidate models and ship to production. Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate

12+ years in backend or platform engineering, with substantial experience building production ML systems or data-intensive infrastructure. Strong programming skills in Java, Kotlin, Scala, and/or Python. Deep understanding of ML systems design: feature stores, training/serving consistency, model versioning, and online/offline inference pipelines. Experience with high-scale batch and real-time data pipelines (Spark, Airflow, Kafka, or equivalent), including point-in-time correctness for backfills. Expertise with architectural patterns of large, high-scale applications — well-designed APIs, efficient data contracts, multi-tenant serving infrastructure. Proven ability to lead cross-team technical initiatives spanning ML and platform engineering.

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. 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. Architect backfill and evaluation infrastructure so the modeling team can simulate production inference over historical data in days, not weeks. Establish domain contracts between Modeling and Serving so each team can move independently with clear, enforced interfaces. Review and evolve the ML serving architecture — making tradeoff calls on feature pipeline design, model composition, and API interfaces. Write and review code for feature engineering jobs, feature store configurations, and serving service endpoints. Partner with Data Science, MLE, MLI and core Pricing & Availability systems BE teams to define artifact handoffs and integration contracts. Drive milestone planning across the Host Pricing & Settings org, sequencing work to deliver value incrementally. Mentor engineers through design reviews and hands-on pairing on the hardest infrastructure problems.