Machine Learning Engineer II, Hyderabad
Warner Bros. (Warner Bros. Entertainment Inc.)Match Score
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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, metro Hyderabad, and a mid-level generalist ML role increase candidate competition.
Production ML skills are transferable but ad-tech/identity specialization makes industry fit moderately sensitive.
Explicit 3–5 years plus production ML, Databricks/AWS/PyTorch requirements indicate high shortlisting strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Build and operate production ML systems for identity resolution, personalization, advertising, forecasting, and audience intelligence across WBD consumer platforms.
Develop and maintain ML pipelines, models, and services including low-latency online serving, feature pipelines combining streaming and batch data, and MLOps workflows with monitoring and governance.
Collaborate cross-functionally with senior engineers, data scientists, product managers, and platform teams to improve model quality, production readiness, and technical delivery of ML systems.
Minimum Requirements
3–5 years of ML engineering experience, or 2+ years with a Ph.D., including ownership of production ML components.
Bachelor’s or master’s degree in computer science, statistics, machine learning, or related field, or equivalent industry experience.
Strong hands-on experience with Databricks, Spark, AWS SageMaker, Python, and SQL.
Proficiency in ML frameworks such as PyTorch, TensorFlow, XGBoost/LightGBM, scikit-learn, and solid understanding of ML fundamentals and statistics.
Ideal Candidate Profile
Experienced in production ML systems spanning the full ML lifecycle: problem definition, feature engineering, model training, serving, monitoring, and iteration.
Background in optimization problems such as ranking, personalization, forecasting, and multi-objective decisioning in complex environments.
Familiarity with streaming data, real-time feature serving, low-latency inference, identity resolution, audience modeling, and graph neural networks or mixture-of-experts models.