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Warner Bros. Discovery
Senior Machine Learning Engineer (Data & Audience Platform Team), Hyderabad
Warner Bros. Discovery
Hyderabad•On-site•5-8 Yrs•Jun 21
Job description
Overview
Welcome to Warner Bros. Discovery… the stuff dreams are made of.
Responsibilities
- 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).
- 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).
- 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.
- Embed FinOps cost discipline (compute caps, auto-termination, job tagging) into pipeline design.
- Actively use and advocate for AI-assisted development: Cursor, GitHub Copilot, and Amazon Q for code generation, review, and documentation.
- Leverage Databricks Genie as a governed natural-language analytics layer — configuring Genie Spaces over ML feature tables and audience datasets to enable self-service exploration for cross-functional stakeholders.
- Use Snowflake Cortex (Copilot, Cortex Analyst, Cortex Search) to accelerate SQL authoring, data discovery, and RAG-based internal tooling over Snowflake-resident identity and audience data.
- Prototype agentic ML workflows (e.g., with MCP-compatible tooling, LangChain/LangGraph) to automate repetitive tasks such as data validation, feature selection, and hyperparameter search; evaluate LLM-based approaches for metadata enrichment and content understanding.
- Mentor MLE 2s through code reviews, design discussions, and pairing; contribute to team technical standards.
- Partner with Product, Marketing, and Ad Sales to translate business requirements into ML problem formulations, and with Data Engineering on data contracts and pipeline SLAs.
- Communicate model performance, trade-offs, and business impact clearly to technical and non-technical stakeholders.
- Identity Intelligence — foundational, privacy-safe identity across all WBD brands: probabilistic ID resolution that resolves unauthenticated signals to households/persons with calibrated confidence (entity resolution with gradient boosting and embeddings, representation learning, isotonic calibration, candidate blocking, champion/challenger pipelines), expanding addressable audiences beyond deterministic matching.
- Audience Intelligence — advertising and marketing use cases: lookalike and predictive audiences (LAL across 1,000+ features), ML-driven smart audiences, layered retrieval + propensity, and incrementality/closed-loop optimization, with privacy-safe activation including data clean rooms.
- ML-based Forecasting — audience growth, demand, and advertising yield/pricing forecasting that powers ad sales and marketing decisions.
- Content Preferences & Affinity — genre-preference, content-preference, and single-title affinity modeling (two-tower retrieval with semantic content embeddings) that ranks audiences for upcoming titles and powers cross-channel promotion.
Qualifications
- 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.
- 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.
- Recommendation systems, personalization, identity resolution, or audience modeling in a media / streaming / ad-tech context.
- Familiarity with Data Clean Room environments (Snowflake DCR, AWS Clean Rooms) and consent-aware activation.
- Hands-on experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, MCP), Databricks Genie Space configuration, and Snowflake Cortex.
Benefits
- A Great Place to work
- Equal opportunity employer
- Fast track growth opportunities