About Warner Bros. Discovery
Warner Bros. Discovery, a premier global media and entertainment company, offers audiences the world's most differentiated and complete portfolio of content, brands and franchises across television, film, streaming and gaming. The new company combines Warner Media’s premium entertainment, sports and news assets with Discovery's leading non-fiction and international entertainment and sports businesses.
For more information, please visit www.wbd.com.
Meet our Team
Warner Bros. Discovery (WBD) is home to the world’s most iconic entertainment, news, and sports brands — HBO Max, CNN, Discovery+, DC, Warner Bros., Bleacher Report, Food Network, and many more. Within the Data & Audience Platform (DAP) organization, our Machine Learning Engineering team in Hyderabad builds the foundational AI/ML intelligence that powers identity, audience, advertising, and personalization across every WBD brand. We turn first-party signals from hundreds of millions of viewers into production ML systems that expand addressable audiences, sharpen targeting and measurement, forecast demand, and personalize content discovery — directly driving advertising yield, marketing efficiency, engagement, and retention.
At WBD, MLEs do rigorous data science and own the engineering that brings models to life: production ML data pipelines, model training and optimization, and the ML infrastructure — feature stores, training and serving pipelines, and MLOps — that makes our work reliable, repeatable, and scalable. We build primarily on Databricks, with strong working knowledge of Snowflake and AWS, and we are an early, enthusiastic adopter of agentic AI development workflows.
About the Role
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.
Roles & 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.
Embed FinOps cost discipline (compute caps, auto-termination, job tagging) into pipeline design.
Mentorship & Cross-functional Collaboration
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.
Flagship Projects You’ll Work On
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.
What You’ll Bring
Required
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 (SageMaker, 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 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.
Preferred:
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.
Experience with two-tower / retrieval architectures, probabilistic identity resolution (graph-based matching, entity resolution), and probability calibration.
Hands-on experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, MCP), Databricks Genie Space configuration, and Snowflake Cortex.
Experience with feature stores (Databricks Feature Store, Tecton, Feast) and contributions to open source or ML publications.
What We Offer:
