Staff Machine Learning Engineer, Data & Audience Platform

Staff Machine Learning Engineer architecting ML capabilities for identity and audience intelligence at Warner Bros. Discovery.

Posted 6/18/2026full-timeHyderabad • 🇮🇳 IndiaLeadWebsite

Tools & technologies

AWSPyTorchScikit-LearnSQLTensorflowUnity

Key responsibilities & impact

Define and own the technical architecture for the team’s core systems: the probabilistic identity spine, audience intelligence platform, content-affinity and genre-preference models, and ML-based forecasting
Lead architectural decisions for the team’s MLOps framework — feature-store design, training-pipeline standards, model-serving patterns, and monitoring infrastructure — on a Databricks-first architecture, integrating Snowflake and AWS SageMaker where each is the right tool
Architect and lead delivery of the probabilistic identity resolution system — resolving unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence at scale across all WBD brands — using entity resolution, embeddings/representation learning, calibration, candidate blocking, and champion/challenger promotion
Define the team’s MLOps target architecture: feature contracts, model-registry governance, automated retraining, drift detection, and A/B experimentation infrastructure
Lead the team’s adoption of agentic AI development: define standards for using Cursor, GitHub Copilot, and Amazon Q in production ML workflows, and for MCP-based tooling

What you’ll need

8+ years of industry experience in ML engineering (6+ with a Ph.D.)
Mastery of the full ML stack: data engineering, feature engineering, model development, MLOps, and production monitoring
Deep Databricks expertise: Delta Lake, Unity Catalog, Workflows/DLT, MLflow, Feature Store, Asset Bundles, and Genie Space configuration
Strong AWS proficiency (SageMaker training/pipelines/model registry, S3, Lambda, Glue) and Snowflake expertise (DCR patterns, Snowpark, Cortex, SQL optimization at scale)
Proven experience architecting production ML systems serving millions of users, and a track record of technical leadership (setting standards, driving architecture, influencing across teams)
Expert proficiency with ML frameworks (PyTorch, TensorFlow, XGBoost/LightGBM, scikit-learn) and deep understanding of statistics and ML fundamentals
Excellent communication, with the ability to advocate technical solutions to engineering, science, product, and executive audiences.

Comp & perks

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Hard Skills & Tools

MLOpsdata engineeringfeature engineeringmodel developmentproduction monitoringentity resolutionembeddingscalibrationA/B experimentationstatistics

Soft Skills

technical leadershipcommunicationadvocacy