Senior Staff Software Engineer, Host Pricing & Settings

Навыки

• Airflow
• API (интеграции)
• Java
• Kafka
• Kotlin
• Machine Learning
• Python

О компании и продукте

• 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.
• 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.

Задачи

• 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

Требования

• 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

Будет плюсом

• 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

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