Resumen del rol

Sr. ML Engineer

Requisitos y responsabilidades

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Responsibilities

  • Convert data science prototypes (propensity, lookalike, segmentation, fusion) into reproducible, production-quality ML services.
  • Build and operate large-scale data/feature pipelines on Databricks + Spark, reading from and writing to Snowflake, producing versioned audience segments on schedule.
  • Own the full model lifecycle: MLflow tracking/registry, CI/CD (Azure DevOps or GitHub Actions), automated retraining, drift monitoring, and rollback.
  • Engineer the activation layer, governed delivery of segments and scores into DSPs/SSPs, DMP/CDP, clean rooms, and CTV/social/programmatic partners, including identity resolution and onboarding.
  • Ensure scalability, latency, cost-efficiency, and reproducibility across Azure (ADLS, Azure ML, AKS); support batch and near-real-time scoring.
  • Enforce privacy-by-design: data minimization, access controls, encryption, and clean-room-compatible patterns.

Essential qualifications

  • 8–9 years hands-on ML engineering, shipping and operating production systems at scale.
  • Prior experience in media / advertising / audience activation.
  • Databricks (incl. MLflow and Spark), Snowflake, and production Azure (ADLS, Azure ML, Azure DevOps, AKS).
  • Expert Python and ML ecosystem; strong SQL at scale.
  • Strong applied statistics (sampling/weighting, forecasting, causal inference, or experimental design).
  • CS/engineering degree with strong quantitative foundations.

Desirable

  • Agile practices, Docker/Kubernetes, advanced Databricks/Snowflake (Structured Streaming, tuning, Model Serving/Feature Store), orchestration (Databricks Workflows, Airflow, Dagster).
  • Ad tech/identity experience: DSP/SSP, DMP/CDP, clean rooms (LiveRamp, Snowflake), identity graphs, onboarding, delivery APIs.
  • AWS/GCP a plus.
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FocoML EngineeringÁrea del rol
Señal de senioritySeniorNivel del candidato
StackAWS, Azure, CI/CDSkills principales
Ubicación27 países aceptadosElegibilidad

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