Machine Learning Engineer II ( Growth & ML) New
This role is listed from Warnerbros’s careers page. You apply on their site.
Log in to applyStorytelling is, and will always be, the heart of what we do at Skydance.
About the role
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!
ㅤ
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.
About the Role
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.
What You’ll Do
ML Development & Modeling
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.
ML Infrastructure & Engineering
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.
Agentic AI & Modern Tooling
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.
Collaboration & Growth
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.
What You’ll Bring
Required
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.
Preferred
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
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.
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!
ㅤ
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.
About the Role
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.
What You’ll Do
ML Development & Modeling
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.
ML Infrastructure & Engineering
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.
Agentic AI & Modern Tooling
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.
Collaboration & Growth
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.
What You’ll Bring
Required
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.
Preferred
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
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.
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