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Strong global brand, metro location, popular ML title, and broad skillset requirements drive high competition.
Highly specialized ML, Databricks, Snowflake, ad-tech identity-resolution focus limits cross-industry transferability.
Explicit 8+ years, Staff-level impact, and deep Databricks/Snowflake/MLOps mastery create very strict shortlisting filters.
Lead technical architecture and design for core ML systems including identity resolution, audience intelligence, content affinity, and forecasting platforms.
Own the end-to-end ML infrastructure and MLOps standards on a Databricks-centric stack integrating Snowflake and AWS tools, ensuring scalable, production-grade deployments.
Provide architectural leadership, mentor senior engineers, and influence cross-functional teams on ML engineering best practices and strategic roadmap decisions.
8+ years in machine learning engineering with demonstrated Staff-level technical leadership and impact.
Expertise in full ML stack including data engineering, feature engineering, model training, deployment, monitoring, and MLOps using Databricks, Snowflake, and AWS.
Master’s or Ph.D. in Computer Science, Statistics, Machine Learning, or related field, or equivalent industry experience.
Proficiency with ML frameworks such as PyTorch, TensorFlow, XGBoost/LightGBM, and strong statistical/ML fundamentals.
Experienced in architecting and scaling production ML systems serving millions of users, particularly in streaming, media, or ad-tech domains.
Strong knowledge of probabilistic identity resolution and data clean room ML technologies with hands-on experience implementing complex ML pipelines.
Effective at setting technical standards and driving cross-team alignment on ML practices and infrastructure within large, multi-disciplinary engineering organizations.