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Senior Staff Software Engineer, Host Pricing & Settings

Rol remoto de Software Engineering con fit claro de ubicación del candidato.

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Match de stackJava, Python

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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.

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.

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