Senior Machine Learning Engineer(Growth & ML), Hyderabad

Overview

As a Senior Machine Learning Engineer, you will design and deliver production ML systems that support audience targeting, advertising revenue, subscriber engagement, and retention across Warner Bros. Discovery’s global portfolio. You will lead technical workstreams, independently drive projects from problem framing through production deployment and monitoring, and mentor MLE 2s.

What you'll do

• Lead end-to-end development of production ML systems, including data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
• Own ML products including probabilistic identity resolution, single-title affinity, and audience or propensity models.
• Design scalable feature pipelines on Databricks using PySpark, Delta, Workflows/DLT, Unity Catalog, and the WBD feature store.
• Document feature contracts, backfill paths, and freshness SLAs.
• Architect batch and near-real-time inference pipelines integrated with Snowflake and activation systems including Mosaic, FreeWheel, and GAM.
• Develop and optimize gradient boosting, embedding/two-tower retrieval, neural ranking, probability calibration, and probabilistic or graph-based matching models.
• Design offline and online experiments and define evaluation frameworks suited to each use case.
• Apply causal-inference techniques to measure the true lift of audience targeting on engagement and retention KPIs.
• Contribute to lookalike modeling using 1,000+ first- and third-party features and privacy-safe builds inside Snowflake Data Clean Rooms.
• Champion MLOps 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 SageMaker where appropriate.
• Enforce leakage prevention and training/serving consistency.
• Implement data-quality checks, model-health dashboards, and alerting thresholds.
• Embed FinOps cost discipline through compute caps, auto-termination, and job tagging.
• Mentor MLE 2s through code reviews, design discussions, and pairing, and contribute to technical standards.
• Partner with Product, Marketing, Ad Sales, and Data Engineering to translate requirements into ML problem formulations and data or pipeline SLAs.
• Communicate model performance, trade-offs, and business impact to technical and non-technical stakeholders.
• Work on identity intelligence, audience intelligence, ML-based forecasting, and content preferences and affinity projects.

What you'll need

• 5–8 years of industry experience in ML engineering or applied data science, or 3+ years with a Ph.D.
• Deep Python expertise and production-quality software engineering practices.
• Production experience building and deploying ML at scale for millions+ of users or records.
• Strong proficiency in Databricks, including PySpark, Delta Lake, Workflows/DLT, MLflow, and Unity Catalog.
• Solid SQL and Snowflake experience for feature sourcing and model-output delivery.
• Experience with AWS ML services, including SageMaker, S3, and Lambda.
• Strong understanding of ML model evaluation, A/B testing, and statistical inference.
• Knowledge in one or more of recommendations and ranking, identity resolution, embeddings and retrieval, causal or 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.

Nice to have

• Recommendation systems, personalization, identity resolution, or audience modeling in a media, streaming, or ad-tech context.
• Familiarity with Data Clean Room environments, including Snowflake DCR and AWS Clean Rooms, and consent-aware activation.
• Experience with two-tower or retrieval architectures, probabilistic identity resolution, graph-based matching, entity resolution, and probability calibration.
• Hands-on experience with agentic AI frameworks including LangChain, LangGraph, AutoGen, and MCP.
• Databricks Genie Space configuration and Snowflake Cortex experience.
• Experience with feature stores including Databricks Feature Store, Tecton, and Feast.
• Contributions to open source or ML publications.

Details

• Location: Hyderabad, at Phoenix Equinox Tower 2.