Staff Machine Learning Engineer (Data & Audience Platform Team), Hyderabad

About the role

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.

Responsibilities

• 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.
• Flagship ML System Ownership
• Own the ML architecture for forecasting (audience growth, demand, yield/pricing) and ensure models are production-grade, monitored, and continuously improved.
• Drive the roa

Benefits

Additional Information

Welcome to Warner Bros. Discovery... the stuff dreams are made of. Who We Are... 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. Staff Machine Learning Engineer (Data & Audience Platform), Hyderabad About Warner Bros. Discovery 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 . Meet our Team 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.

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