Lead end-to-end development of production ML systems: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
Own key ML products such as probabilistic identity resolution, single-title affinity, and audience/propensity models.
Design scalable feature pipelines on Databricks and the WBD feature store, with documented feature contracts, backfill paths, and freshness SLAs.
Architect batch and near-real-time inference pipelines integrated with Snowflake and activation systems.
Develop and optimize models across the ML spectrum: gradient boosting, embedding/two-tower retrieval, neural ranking, probability calibration, and probabilistic/graph-based matching.
Design rigorous offline and online experiments; define evaluation frameworks appropriate to each use case.
Contribute to lookalike modeling using 1,000+ first
and third-party features.
Champion MLOps best practices: model versioning, automated retraining triggers, drift detection, and production monitoring with MLflow.
Build and maintain robust, reproducible, auditable ML pipelines on Databricks and enforce leakage prevention and training/serving consistency.
Mentor MLE 2s through code reviews, design discussions, and pairing.

5–8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.)
Deep Python expertise and production-quality software engineering practices; production experience building and deploying ML at scale (millions+ of users/records)
Strong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT, MLflow, Unity Catalog) and solid SQL/Snowflake experience for feature sourcing and model-output delivery
Experience with AWS ML services (SageMaker, S3, Lambda)
Strong understanding of ML model evaluation, A/B testing, and statistical inference; knowledge in one or more of recommendations & ranking, identity resolution, embeddings/retrieval, causal/interpretable ML, forecasting, bandits, or optimization
Demonstrated ability to lead technical decisions and mentor engineers.
Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent experience)
Excellent written and verbal communication, with the ability to advocate technical solutions to engineers, scientists, and product stakeholders.

Senior Machine Learning Engineer, Data & Audience Platform Team

Key skills