Senior Staff Software Engineer, Host Pricing & Settings

Job Overview

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. This role involves owning the technical strategy for the full Modeling → ML Serving → API interface across the Host Pricing org.

Responsibilities

• 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.
• Arcitect 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.

Requirements

• 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.
• 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.

Qualifications

• Master's degree in Computer Science, Statistics, Mathematics, or related field.
• Experience with machine learning libraries such as TensorFlow, PyTorch, or Scikit-Learn.
• Experience with cloud platforms like AWS, Google Cloud, or Azure.
• Experience with distributed systems and microservices architecture.
• Experience with CI/CD pipelines and automated testing.

Skills

• Experience with machine learning libraries such as TensorFlow, PyTorch, or Scikit-Learn.
• Experience with cloud platforms like AWS, Google Cloud, or Azure.
• Experience with distributed systems and microservices architecture.
• Experience with CI/CD pipelines and automated testing.

Benefits

• Flexible remote work option.
• Competitive compensation package.
• Health, dental, and vision insurance.
• 401(k) retirement plan.
• Generous paid time off.
• Professional development opportunities.

Pay

Compensation is competitive and commensurate with experience.

Schedule

This role is 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.