Senior Staff Software Engineer, Host Pricing & Settings

About the role

Staff Software Engineer, Host Pricing & Settings

Airbnb began in 2007 when two hosts welcomed three guests into their San Francisco home. Today, that community includes over 5 million hosts who have welcomed more than 2 billion guest arrivals across nearly every country. Hosts provide unique stays and experiences that help guests connect with local communities in an authentic way.

The Host Pricing & Settings team builds the platform and tools that help hosts run their businesses. Pricing strategies draw on market intelligence, comparable listings, and demand signals. The team collaborates closely with Search, Listings, Tax, and Payments to ensure guidance remains accurate, timely, and trusted.

Behind every pricing recommendation is a sophisticated ML system undergoing fundamental rearchitecture. The north star is a serving infrastructure where training, inference, and evaluation are consistent by design. Features come from a centralized store. Model composition happens in one place. Backfills are available on demand so data scientists and MLEs can evaluate candidates in days, not weeks.

As a senior technical individual contributor, you will own the technical strategy for the full Modeling → ML Serving → API interface across the Host Pricing organization. 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.

What You Will Do

You will define the architecture and contracts that govern how models move from development to production. This includes feature store design, model schema management, online and offline inference consistency, and multi-version support.

You will lead the buildout of a unified serving stack. The goal is to eliminate per-model one-off implementations and provide a standard path from training to production for data scientists.

You will architect backfill and evaluation infrastructure. This allows the modeling team to simulate production inference over historical data in days, not weeks.

You will establish domain contracts between Modeling and Serving. These contracts let each team move independently while maintaining clear, enforced interfaces.

You will review and evolve ML serving architecture. This involves making tradeoff decisions on feature pipeline design, model composition, and API interfaces.

You will write and review code for feature engineering jobs, feature store configurations, and serving service endpoints.

You will partner with Data Science, MLE, MLI, and core Pricing & Availability systems teams. You will define artifact handoffs and integration contracts.

You will mentor engineers through design reviews and hands-on pairing on the hardest infrastructure problems.

You will drive milestone planning across Host Pricing & Settings. You will sequence work to deliver value incrementally.

You will champion point-in-time correctness for pipelines. This ensures backfills remain reliable at scale across batch and real-time flows.

Your Expertise

You have 12+ years in backend or platform engineering, with substantial experience building production ML systems or data-intensive infrastructure.

You have strong programming skills in Java, Kotlin, Scala, and Python. You maintain clean API contracts and versioning discipline.

You understand feature stores, model versioning, and online and offline inference pipelines. You enforce consistency by design.

You handle high-scale batch and real-time pipelines with Spark, Airflow, Kafka, or similar. This includes point-in-time correctness for backfills.

You can design large-scale application architectures. You create efficient data contracts and multi-tenant serving infrastructure.

You have a proven ability to lead cross-team technical initiatives that span ML modeling, MLI, and core Pricing and Availability systems teams.

Preferred Qualifications

You have production experience with feature stores such as Chronon, Tecton, or Feast. This includes backfill automation and serving consistency.

You have experience with model schema management, multi-version support, and model composition frameworks.

You have a track record defining and enforcing technical contracts between modeling, MLI, serving teams, and product surfaces.

You have a record of improving evaluation speed for ML teams. This is measured by faster candidate testing and shorter iteration cycles.

Skills & Tools

Java, Kotlin, Scala, Python, Spark, Airflow, Kafka, Feast, Tecton, Chronon

What you'll do

• Meet the bar