Sr Machine Learning Engineer (Growth & ML)

Job Summary

Senior Machine Learning Engineer (Growth ML), Hyderabad

As a Senior MLE, you will own the design and delivery of production ML systems that directly impact audience targeting, advertising revenue, subscriber engagement, and retention across WBD s global portfolio. You will lead technical execution on key workstreams - including probabilistic identity resolution, lookalike modeling, single-title affinity, and forecasting - while mentoring MLE 2s and collaborating closely with Staff MLEs and product stakeholders. This is a high-ownership role for engineers with roughly 5-8 years of experience who can independently drive a project from problem framing through production deployment and monitoring.

Responsibilities

ML System Design Ownership

• 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 (matching unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence), single-title affinity (e.g., STAT two-tower retrieval), and audience/propensity models.

• Design scalable feature pipelines on Databricks (PySpark, Delta, Workflows/DLT, Unity Catalog) 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 (Mosaic, FreeWheel, GAM).

  Modeling Experimentation

• Develop and optimize models across the ML spectrum: gradient boosting (XGBoost/LightGBM), embedding/two-tower retrieval, neural ranking, probability calibration (e.g., isotonic regression), and probabilistic/graph-based matching.

• Design rigorous offline and online experiments; define evaluation frameworks (precision/recall, AUC-ROC, NDCG, decile lift, calibration curves) appropriate to each use case.

• Apply causal-inference techniques (propensity scoring, uplift/incrementality modeling) to measure true lift of audience targeting on engagement and retention KPIs.

• Contribute to lookalike modeling (LAL 2.0+) using 1,000+ first- and third-party features, including privacy-safe builds inside Data Clean Rooms (Snowflake DCR).

  MLOps Infrastructure

• Champion MLOps best practices: model versioning, champion/challenger promotion, automated retraining triggers, drift detection, and production monitoring with MLflow on Databricks.

• Build and maintain robust, reproducible, auditable ML pipelines on Databricks (and AWS SageMaker where appropriate, e.g., the identity-resolution track); enforce leakage prevention and training/serving consistency.

• Contribute to the team s feature-store strategy - feature contracts, backfills, and freshness SLAs - and implement data-quality checks, model-health dashboards, and alerting thresholds.

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