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Strong global brand, metro location, mid-level ML role, and broad skills increase competition.
Specialized ML engineering and production MLOps requirements make cross-industry fit limited.
Explicit 5–8 years requirement plus specific ML, Databricks/AWS/Snowflake stack and PhD preference enforces strict screening.
Design and operate low latency online ML systems for fraud scoring, message decisioning, and real-time personalization.
Architect and lead production ML systems for identity resolution, audience intelligence, personalization, forecasting, and engagement across global markets.
Mentor ML engineers and collaborate cross-functionally to translate business needs into scalable ML solutions, ensuring quality, observability, and continuous improvement.
5-8 years of ML engineering experience (or 2+ years with a Ph.D.) with senior-level technical ownership and cross-team impact.
Master's or Ph.D. in Computer Science, Statistics, Machine Learning, or related field (or equivalent industry experience).
Hands-on experience with Databricks, Spark, AWS Sagemaker, and ML frameworks like PyTorch, TensorFlow, XGBoost, LightGBM, scikit-learn.
Experience building production ML systems for large user populations including real-time feature serving and low-latency inference.
Strong expertise in optimization problems such as ranking, personalization, and multi-objective decisioning with interacting metrics.
Experienced in architecting and scaling ML infrastructure supporting identity resolution, audience modeling, and ad-tech ML domains.
Proven ability to lead technical roadmap, mentor others, communicate complex ML concepts to diverse stakeholders, and influence cross-team standards.