Senior Machine Learning Engineer ML), Hyderabad

Within Warner Bros. Discovery's Data & Audience Platform (DAP) organisation, the Hyderabad Machine Learning Engineering team builds the AI/ML intelligence that powers identity, audience, advertising and personalisation across WBD brands. The Senior Machine Learning Engineer 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. The role will lead technical execution on work streams such as probabilistic identity resolution, lookalike modelling, single-title affinity and forecasting, while mentoring MLE 2s and collaborating with Staff MLEs and product stakeholders.

It is a high-ownership role for engineers with roughly 5-8 years of experience who can independently drive projects from problem framing through production deployment and monitoring.

• Lead end-to-end development and delivery of production ML systems, covering data sourcing, feature engineering, model training, evaluation, deployment and monitoring.
• Own key ML products including probabilistic identity resolution, single-title affinity (e.g., STAT two-tower retrieval), audience/propensity models and lookalike modelling (LAL 2.0+) using 1,000+ first- and third-party features, including privacy-safe builds inside Data Clean Rooms.
• 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, Free Wheel, GAM).
• Develop and optimise models across the ML spectrum, including 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 and 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 modelling) to measure true lift of audience targeting on engagement and retention KPIs.
• Champion MLOps best practices, including 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 Sage Maker where appropriate), enforcing leakage prevention and training/serving consistency, and embed Fin Ops cost discipline (compute caps, auto-termination, job tagging) into pipeline design.
• Contribute to the team's feature-store strategy and implement data-quality checks, model-health dashboards and alerting thresholds.
• Mentor MLE 2s through code reviews, design discussions and pairing
• Contribute to team technical standards
• Partner with Product, Marketing and Ad Sales on ML problem formulation and with Data Engineering on data contracts and pipeline SLAs
• Communicate model performance, trade-offs and business impact clearly.

• 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 (Sage Maker, 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 optimisation.
• 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.

• Recommendation systems, personalisation, identity resolution, or audience modelling in a media / streaming / ad-tech context.
• Familiarity with Data Clean Room environments (Snowflake DCR,…