Staff Machine Learning Engineer - (Growth & ML Team), Hyderabad

When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next…

From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.

Warner Bros. Discovery, a premier global media and entertainment company, offers audiences the world's most differentiated and complete portfolio of content, brands and franchises across television, film, streaming and gaming. The new company combines Warner Media’s premium entertainment, sports and news assets with Discovery's leading non-fiction and international entertainment and sports businesses.

For more information, please visit www.wbd.com.

Warner Bros. Discovery (WBD) is home to the world’s most iconic entertainment, news, and sports brands — HBO Max, CNN, Discovery+, DC, Warner Bros., Bleacher Report, Food Network, and many more. Within the Data & Audience Platform (DAP) organization, our Machine Learning Engineering team in Hyderabad builds the foundational AI/ML intelligence that powers identity, audience, advertising, and personalization across every WBD brand. We turn first-party signals from hundreds of millions of viewers into production ML systems that expand addressable audiences, sharpen targeting and measurement, forecast demand, and personalize content discovery — directly driving advertising yield, marketing efficiency, engagement, and retention.

At WBD, MLEs do rigorous data science and own the engineering that brings models to life: production ML data pipelines, model training and optimization, and the ML infrastructure — feature stores, training and serving pipelines, and MLOps — that makes our work reliable, repeatable, and scalable. We build primarily on Databricks, with strong working knowledge of Snowflake and AWS, and we are an early, enthusiastic adopter of agentic AI development workflows.

The Staff MLE is the senior-most individual contributor on the Hyderabad ML Engineering team. You will set the technical direction for the team’s most complex and strategically important ML systems, serve as the technical authority across multiple concurrent workstreams, and act as a force multiplier for the entire team. You will own the architecture of WBD’s ML capabilities in Hyderabad — spanning identity intelligence, audience intelligence, content affinity, and forecasting — and be a key technical partner to the Senior ML Engineering Manager and Director. This role requires 8+ years of experience, exceptional depth across the ML stack, and the ability to influence technical decisions across organizational boundaries.

Technical Vision & Architecture

• →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.
• →Evaluate and recommend new technologies and approaches (e.g., DCR-native modeling, graph ML, agentic ML orchestration, LLM-augmented pipelines) with clear build/buy/partner assessments.
• →Drive standardization of ML practices across Hyderabad and align with global WBD ML engineering standards.

• 8+ years of industry experience in ML engineering (6+ with a Ph.D.), with demonstrated Staff-level scope and impact.
• 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.
• Master’s or Ph.D. in Computer Science, Statistics, Machine Learning, or a related field (or equivalent industry experience).
• Excellent communication, with the ability to advocate technical solutions to engineering, science, product, and executive audiences.

• Streaming / media / ad-tech ML: identity resolution, audience modeling, recommendation/ranking, content understanding.
• Deep knowledge of probabilistic identity resolution (entity resolution, graph-based household matching, confidence calibration) and Data Clean Room ML (Snowflake DCR, AWS Clean Rooms).
• Hands-on experience with agentic AI frameworks at production scale (LangGraph, AutoGen, MCP, CrewAI), Databricks Genie Space curation, and Snowflake Cortex Search / Fine-Tuning for enterprise RAG.
• Experience with real-time feature serving and low-latency inference, and with mixture-of-experts or graph neural networks.
• Published research or conference presentations in relevant ML domains.

This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.

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