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. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.