FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Senior Machine Learning Engineer, Data & Audience Platform Team
Warner Bros. DiscoverySenior ML Engineer leading and developing production ML systems impacting audience targeting and advertising revenue at Warner Bros. Discovery.
Tech Stack
Tools & technologiesAWSPySparkPythonSQLUnity
About the role
Key responsibilities & impact- 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, single-title affinity, and audience/propensity models.
- Design scalable feature pipelines on Databricks 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.
- Develop and optimize models across the ML spectrum: gradient boosting, embedding/two-tower retrieval, neural ranking, probability calibration, and probabilistic/graph-based matching.
- Design rigorous offline and online experiments; define evaluation frameworks appropriate to each use case.
- Contribute to lookalike modeling using 1,000+ first- and third-party features.
- Champion MLOps best practices: model versioning, automated retraining triggers, drift detection, and production monitoring with MLflow.
- Build and maintain robust, reproducible, auditable ML pipelines on Databricks and enforce leakage prevention and training/serving consistency.
- Mentor MLE 2s through code reviews, design discussions, and pairing.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- Fast track growth opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learning engineeringfeature engineeringmodel trainingmodel evaluationmodel deploymentmodel optimizationA/B testingstatistical inferencePythonSQL
Soft Skills
mentoringtechnical decision-makingcommunication
Certifications
Ph.D.Bachelor's degreeMaster's degree