Senior Staff Software Engineer, Host Pricing & Settings

Responsibilities

• Own the technical strategy for the full Modeling → ML Serving → API interface across the Host Pricing org.
• Define the architecture and contracts governing how models move from development to production, including 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.

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.

Nice-to-Haves

• Production experience with Chronon, Tecton, Feast, or equivalent — including online/offline consistency and backfill automation.
• Experience with model schema management, multi-version support, and model composition frameworks.
• Track record defining and enforcing technical contracts between ML modeling, MLI, serving teams and/or product surfaces.
• Measurable impact improving the speed at which ML teams evaluate candidate models and ship to production.