WD
Listed on Warner Bros. Discovery’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
Warner Bros. Discovery seeks a senior machine learning engineer to design and own production ML systems powering identity resolution, audience targeting, and personalization across their streaming platforms. This role bridges technical leadership and hands-on modeling work, requiring someone comfortable mentoring globally distributed teams while driving architectural decisions for flagship products serving hundreds of millions of viewers.
Our summary, not Warner Bros. Discovery’s wording. The full posting is on their site.
Skills this role names
- A/B Testing
- Amazon Web Services (AWS)
- Cursor AI
- Databricks
- GitHub Copilot
- LangChain
- LangGraph
- MLflow
- PySpark
- Python
- Snowflake
- SQL
- XGBoost
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What they ask for
Required
- 5–8 years of ML engineering or applied data science experience (or 3+ years with PhD)
- Track record of leading projects to production
- Deep Python expertise and strong software engineering practices
- Production experience with ML at scale (millions+ of users/records)
- Strong proficiency in Databricks including PySpark, Delta Lake, Workflows/DLT, MLflow, and Unity Catalog
- Solid SQL and Snowflake experience for feature sourcing and model delivery
- Experience with AWS ML services (SageMaker, S3, Lambda)
- Strong understanding of ML model evaluation, A/B testing, and statistical/causal inference
- Depth in at least one of: recommendations and ranking, identity resolution, embeddings/retrieval, forecasting, or optimization
- Demonstrated technical leadership driving architectural decisions and mentoring engineers
- Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or related quantitative field (or equivalent experience)
- Excellent written and verbal communication
Nice to have
- Experience with recommendation systems, personalization, identity resolution, or audience modeling in media/streaming/ad-tech
- Experience with two-tower and retrieval architectures, probabilistic identity resolution, or Data Clean Room ML
- Experience architecting or standardizing ML platform components used across teams
- Hands-on experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, MCP), Databricks Genie Spaces, or Snowflake Cortex
- Experience with feature stores (Databricks Feature Store, Tecton, Feast)
- Open source contributions or ML publications
- Experience mentoring globally distributed teams