Senior Machine Learning Engineer

Senior Machine Learning Engineer – Data & Audience Platform (DAP) Senior, high‑ownership US‑based role that sits between our Senior MLE and Staff MLE levels. The Engineer owns design and delivery of production ML systems end‑to‑end, drives technical leadership, propels architectural decisions, and serves as a US‑based technical anchor for the global team. Responsibilities ML System Design & Technical Leadership Lead end‑to‑end development of production ML systems: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring. Own flagship ML products such as probabilistic identity resolution, single‑title affinity, lookalike modeling, or forecasting. Make and document key architectural decisions across workstreams, providing trade‑off analysis on scalability, latency, reliability, and cost. Design scalable feature and inference pipelines on Databricks (PySpark, Delta, Workflows/DLT, Unity Catalog) integrated with Snowflake and activation systems. Establish and evangelize engineering patterns for others to adopt, anticipating risks and failure modes. Modeling & Experimentation Develop and optimise models: gradient boosting (XGBoost/LightGBM), embeddings/two‑tower retrieval, neural ranking, probability calibration, and probabilistic/graph‑based matching. Design rigorous offline and online experiments, define evaluation frameworks (precision/recall, AUC‑ROC, NDCG, decile lift, calibration curves). Apply causal‑inference techniques (propensity scoring, uplift/incrementality modeling) to measure true lift on engagement and retention KPIs. Contribute to lookalike modeling (LAL 2.0+) using 1,000+ features, including privacy‑safe builds in Data Clean Rooms. 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 reproducible, auditable ML pipelines on Databricks and AWS SageMaker where appropriate, enforcing leakage prevention and training/serving consistency. Shape the feature‑store strategy, implement data‑quality checks, model‑health dashboards, and alert thresholds. Embed FinOps cost discipline (compute caps, auto‑termination, job tagging) into pipeline design. Agentic AI & Modern Development Use and advocate for AI‑assisted development tools (Cursor, GitHub Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP). Configure Databricks Genie Spaces to enable self‑service exploration for stakeholders. Prototype agentic ML workflows using LangChain/LangGraph, evaluate LLM‑based approaches for metadata enrichment. Mentorship & Cross‑functional Collaboration Mentor Senior and MLE 2 engineers, including Hyderabad team members, through code reviews, design discussions, and pairing. Act as US‑based point of contact and time‑zone bridge for the global ML team, aligning priorities across time zones. Partner with US‑based Product, Marketing, and Ad Sales stakeholders to translate business requirements into ML problem formulations. Collaborate 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 Identity Intelligence – Probabilistic ID resolution across all WBD brands. Audience Intelligence – Lookalike and predictive audiences, smart audiences, layered retrieval + propensity, incrementality/closed‑loop optimization. ML‑based Forecasting – Audience growth, demand, and advertising yield/pricing forecasting. Content Preferences & Affinity – Genre‑preference and single‑title affinity modeling with semantic content embeddings. Required Qualifications 5–8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.). Track record of leading projects to production. Deep Python expertise and strong software engineering practices; production‑level 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 model evaluation, A/B testing, and statistical/causal inference; depth in recommendations, rankings, identity resolution, embeddings, forecasting, or optimization. Demonstrated technical leadership: driving architectural decisions, setting patterns/standards, and mentoring engineers 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 skills. Preferred Qualifications Experience with recommendation systems, personalization, identity resolution, or audience modeling in a media / streaming / ad‑tech context. Experience with two‑tower / retrieval architectures, probabilistic identity resolution, 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. Technology Stack Primary platform: Databricks (Lakehouse, PySpark, Delta, Workflows/DLT, MLflow, Feature Store, Unity Catalog, Asset Bundles, Genie). Cloud: AWS (SageMaker, S3, Lambda). Warehouse: Snowflake (incl. DCR, Snowpark, Cortex). Activation: Mosaic, FreeWheel, Google Ad Manager. Agentic AI: Cursor, GitHub Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP. Languages: Python (primary), SQL, Scala (as needed). Compensation Pay Range: $159,180.00 – $295,620.00 per year. Other rewards may include annual bonuses, short‑ and long‑term incentives, and program‑specific awards. Health insurance, employee wellness program, life and disability insurance, retirement savings plan, paid holidays, sick time, and vacation are also part of the package. Equal Employment Opportunity Warner Bros. Discovery is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. #J-18808-Ljbffr