Want to know if this job is worth applying to?
Hyderabad - Phoenix Equinox Tower 2, India
Hybrid
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.
Machine Learning Engineer II ( Data & Audience Platform 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.
As a Machine Learning Engineer II, you will work alongside Senior and Staff engineers to build and maintain production ML pipelines, contribute to model development, and grow into a well-rounded ML engineer who bridges data science and software engineering. This is a hands-on role for someone with roughly 2–4 years of experience who is eager to work on high-impact ML systems at scale — from probabilistic identity graphs to single-title affinity models — and who wants to develop deep expertise across the full ML lifecycle.
Build and maintain end-to-end ML pipelines for training, evaluation, and batch inference across use cases such as identity resolution, audience segmentation, and content affinity modeling.
Implement and experiment with supervised, unsupervised, and ranking models in Python (scikit-learn, XGBoost/LightGBM, PyTorch).
Engineer features from first-party viewership, engagement, subscription, and behavioral signals, guarding against data leakage, collinearity, and training/serving skew.
Run structured offline experiments; evaluate with the right metrics (precision/recall, F1, AUC-ROC, calibration, lift) and document findings in MLflow.
Develop and maintain data and feature pipelines on Databricks (PySpark, Delta, Workflows) that feed the feature store and model-training workflows, with attention to idempotency and reproducibility.
Write clean, tested, production-quality Python following engineering best practices (unit tests, code reviews, CI/CD).
Use MLflow for experiment tracking, model registration, and versioning under the guidance of senior engineers.
Support deployment and monitoring of batch inference jobs integrated with downstream activation platforms (e.g., Mosaic, FreeWheel, GAM) and data in Snowflake.
Use AI-assisted development tools (Cursor, GitHub Copilot, Amazon Q) to accelerate coding, debugging, and documentation under guidance.
Leverage Databricks Genie for natural-language exploration of governed Unity Catalog datasets — querying ML feature tables, model outputs, and audience segments.
Use Snowflake Cortex (Copilot / Cortex Analyst) to accelerate data analysis and SQL authoring against audience and identity schemas. Learn and apply prompt-engineering patterns for LLM-assisted data exploration and feature generation, and participate in evaluating MCP (Model Context Protocol) tooling as the team expands agentic workflows.
Partner with Senior and Staff MLEs to understand system-design decisions and contribute meaningfully to technical discussions.
Work cross-functionally with Data Engineering, Feature Engineering, and Analytics to ensure data quality and pipeline reliability.
Document models, pipelines, and experiments clearly for team knowledge sharing.
2–4 years of industry experience in machine learning, data science, or ML engineering (or 1–2 years with a relevant M.S.).
Strong Python proficiency; experience with pandas, NumPy, scikit-learn, and at least one deep-learning framework (PyTorch or TensorFlow).
Hands-on experience with Spark/PySpark or equivalent large-scale data processing.
Proficiency in SQL and familiarity with cloud data warehouses/lakehouses (Snowflake or Databricks).
Experience with experiment-tracking tools (MLflow, Weights & Biases, or similar).
Solid grasp of core ML concepts: classification, regression, ranking, embeddings, and model evaluation; plus strong CS fundamentals (data structures, algorithms, clean code).
Bachelor’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent practical experience).
Ability to use AI tools to independently improve productivity across the ML lifecycle, and clear written and verbal communication.
Experience with Databricks (notebooks, Delta Lake, Workflows/DLT, Unity Catalog, MLflow).
Exposure to recommendation systems, audience segmentation, identity resolution, or forecasting.
Familiarity with AWS services (SageMaker, S3, Lambda) and/or feature stores (Databricks Feature Store, Feast).
Exposure to agentic AI tools: Cursor, GitHub Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, or MCP.
What We Offer:
A Great Place to work
Equal opportunity employer
Fast track growth opportunities
How We Get Things Done…
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.
Championing Inclusion at WBD
Warner Bros. Discovery embraces 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, regardless of sex, gender identity, ethnicity, age, sexual orientation, religion or belief, marital status, pregnancy, parenthood, disability or any other category protected by 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.