Storytelling is, and will always be, the heart of what we do at Skydance.

Our vision is to build a thriving entertainment ecosystem where stories and worlds are imagined, brought to life and experienced. We create for every age and audience, anchored by the strength of our content and experiences and the depth of our connection with fans. We are creator-first, audience-focused, tech-forward, globally scaled.

Here, you’ll work with talented teams that move fast, think boldly and collaborate with purpose to deliver results. We aim high, champion the best ideas and win together, earning the trust of our employees, creators, partners and audiences every day.

If you’re energized by the opportunity to help build the next-generation global media and entertainment company powered by creativity and technology, we’d be thrilled to welcome you aboard. LET’S GO!

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Staff Machine Learning Engineer (Growth & ML Team), 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.

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.

What You’ll Do

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

• Architect and lead delivery of the probabilistic identity resolution system — resolving unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence at scale across all WBD brands — using entity resolution, embeddings/representation learning, calibration, candidate blocking, and champion/challenger promotion, with a roadmap toward person-level graphs (incl. GNNs) and real-time resolution.
• Lead the evolution of Audience Intelligence: ML Promo Optimizer (layered retrieval + propensity + closed-loop measurement, toward mixture-of-experts), STAT v2 (two-tower single-title affinity with semantic content embeddings), lookalike modeling (LAL 2.0+) inside Snowflake DCR, and content segmentation.
• Own the ML architecture for forecasting (audience growth, demand, yield/pricing) and ensure models are production-grade, monitored, and continuously improved.
• Drive the roadmap for bringing ML personalization signals (genre/content affinity, engagement trends) into batch and, over time, real-time activation paths.

MLOps Platform Leadership

• Define the team’s MLOps target architecture: feature contracts, model-registry governance, automated retraining, drift detection, and A/B experimentation infrastructure.
• Establish engineering standards for the full ML lifecycle: data contracts → feature engineering → training → evaluation → deployment → monitoring → deprecation.
• Champion a lightweight, adoptable, well-documented architecture (Databricks Asset Bundles, GitHub Actions CI/CD, MLflow throughout, feature tables, inference/monitoring tables), with leakage prevention, reproducibility, and FinOps controls baked in.

Organizational Influence & Mentorship

• Serve as the technical anchor for Hyderabad; provide architectural guidance and deep mentorship to Senior and MLE 2 engineers.
• Partner with the Senior ML Engineering Manager on technical roadmap prioritization, headcount planning, and team capability development.
• Represent the Hyderabad team in cross-functional technical forums with US-based ML, Data Engineering, Product, and Ad Sales stakeholders.
• Produce high-quality technical design documents, architecture reviews, and post-mortems that elevate the team’s engineering culture.

What You’ll Bring

Required

• 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.

Preferred

• 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.

Our Technology Stack

Primary platform: Databricks (Lakehouse, PySpark, Delta, Workflows/DLT, MLflow, Feature Store, Unity Catalog, Asset Bundles, Genie).

Cloud: AWS (SageMaker, S3, Lambda, Glue).

Warehouse: Snowflake (DCR, Snowpark, Cortex).

Activation: Mosaic, FreeWheel, Google Ad Manager.

Agentic AI: Cursor, GitHub Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP.

Languages: Python (primary), SQL, Scala (as needed).

What We Offer:

• A Great Place to work
• Equal opportunity employer
• Fast track growth opportunities

About Skydance

Skydance is a next-generation global media and entertainment company, comprised of three business segments: Studios, Direct-to-Consumer, and TV Media. Skydance's portfolio unites legendary brands, including Paramount, Warner Bros., HBO and HBO Max, Paramount+, Pluto TV, CBS, CNN, CBS Sports, TNT Sports, Nickelodeon, Cartoon Network, MTV, Food Network, BET, HGTV, and Comedy Central.

Some Important Details

We embrace the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit. In line with the laws of the country or region in which the role is based, this includes consideration without regard to race, color, ethnicity, national origin, citizenship status, religion or belief, sex, gender, gender identity or expression, sexual orientation, age, marital status, pregnancy, parenthood, physical or mental disability, genetic information, military or protected veteran status, union status or any other category protected by applicable law.

If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.