Senior Machine Learning Engineer (Data & Audience Platform Team), 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.

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.

Agentic AI & Modern Development

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.

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.

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.

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.

This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.

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