Senior Machine Learning Engineer

Warner Bros. Discovery•Burbank, CA

19d•$159,180 - $295,620•Onsite

About The Position

Warner Bros. Discovery (WBD) is seeking a Senior Machine Learning Engineer to join the Data & Audience Platform (DAP) organization. This role is crucial for building foundational AI/ML intelligence that powers identity, audience, advertising, and personalization across all WBD brands. The team transforms first-party viewer signals into production ML systems to enhance audience reach, targeting, measurement, demand forecasting, and content personalization, ultimately driving advertising revenue, marketing efficiency, engagement, and retention. The Machine Learning Engineering team at WBD combines rigorous data science with engineering expertise to deploy models into production, managing ML data pipelines, model training and optimization, and the underlying ML infrastructure including feature stores, training/serving pipelines, and MLOps for reliability, repeatability, and scalability. The primary technology stack includes Databricks, Snowflake, and AWS, with an early adoption of agentic AI development workflows.

Requirements

5–8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.), including a track record of leading projects to production.
Deep Python expertise and strong 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.
Experience with AWS ML services (SageMaker, S3, Lambda).
Strong understanding of ML model evaluation, A/B testing, and statistical/causal inference.
Depth in one or more of recommendations & ranking, identity resolution, embeddings/retrieval, forecasting, or optimization.
Demonstrated technical leadership: driving architectural decisions, setting patterns/standards, and mentoring other engineers — including leading by influence across teams and time zones.
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 Haves

Recommendation systems, personalization, identity resolution, or audience modeling in a media / streaming / ad-tech context.
Experience with two-tower / retrieval architectures, probabilistic identity resolution (graph-based matching, entity resolution, confidence calibration), and Data Clean Room ML (Snowflake DCR, AWS Clean Rooms).
Experience architecting or standardizing components of an ML platform used by multiple engineers or teams.
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.
Experience partnering with or mentoring globally distributed teams.

Responsibilities

Lead end-to-end development of production ML systems: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
Own one or more flagship ML products (e.g., probabilistic identity resolution, single-title affinity, lookalike modeling, or forecasting) and drive their technical direction.
Make and document key architectural decisions across a workstream, providing deep trade-off analysis on scalability, latency, reliability, and cost.
Design scalable feature and inference pipelines on Databricks integrated with Snowflake and activation systems, with documented feature contracts, backfill paths, and freshness SLAs.
Establish and evangelize patterns for other engineers to adopt, anticipating risks and failure modes.
Develop and optimize models across the ML spectrum, including gradient boosting, embedding/two-tower retrieval, neural ranking, probability calibration, and probabilistic/graph-based matching.
Design rigorous offline and online experiments and define appropriate evaluation frameworks.
Apply causal-inference techniques to measure the true lift of audience targeting on engagement and retention KPIs.
Contribute to lookalike modeling using first- and third-party features, including privacy-safe builds inside Data Clean Rooms.
Champion MLOps best practices: model versioning, champion/challenger promotion, automated retraining triggers, drift detection, and production monitoring.
Build and maintain robust, reproducible, auditable ML pipelines on Databricks and AWS SageMaker, enforcing leakage prevention and training/serving consistency.
Shape the team’s feature-store strategy and implement data-quality checks, model-health dashboards, and alerting thresholds.
Embed FinOps cost discipline into pipeline design.
Actively use and advocate for AI-assisted development tools like Cursor, GitHub Copilot, and Amazon Q.
Leverage Databricks Genie for governed natural-language analytics and Snowflake Cortex for accelerating SQL authoring, data discovery, and RAG-based internal tooling.
Design and prototype agentic ML workflows to automate repetitive tasks.
Mentor Senior and MLE 2 engineers, including members of the Hyderabad team, through code reviews, design discussions, and pairing.
Serve as a US-based point of contact and time-zone bridge for the global ML team.
Partner with US-based Product, Marketing, and Ad Sales stakeholders to translate business requirements into ML problem formulations.
Communicate model performance, trade-offs, and business impact clearly to technical and non-technical stakeholders.