{"id":1905718,"url":"https://alion.io/job/warner-bros-discovery-senior-machine-learning-engineergrowth-ml-hyderabad","title":"Senior Machine Learning Engineer(Growth & ML), Hyderabad","company":{"id":21042,"name":"Warner Bros. 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Discovery… the stuff dreams are made of.\nWho We Are…\nWhen 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…\nFrom 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.\n\n Senior Machine Learning Engineer (Growth & ML), Hyderabad\nAbout Warner Bros. Discovery\nWarner 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.\nFor more information, please visit www.wbd.com.\nMeet our Team\nWarner 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.\nAt WBD, MLEs do rigorous data science andown 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.\nAbout the Role\nAs a Senior MLE, you will own the design and delivery of production ML systems that directly impact audience targeting, advertising revenue, subscriber engagement, and retention across WBD’s global portfolio. You will lead technical execution on key workstreams - including probabilistic identity resolution, lookalike modeling, single-title affinity, and forecasting - while mentoring MLE 2s and collaborating closely with Staff MLEs and product stakeholders. This is a high-ownership role for engineers with roughly 5-8 years of experience who can independently drive a project from problem framing through production deployment and monitoring.\nRoles & Responsibilities:\nML System Design & Ownership\nLead end-to-end development of production ML systems: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.\nOwn key ML products such as probabilistic identity resolution (matching unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence), single-title affinity (e.g., STAT two-tower retrieval), and audience/propensity models.\nDesign scalable feature pipelines on Databricks (PySpark, Delta, Workflows/DLT, Unity Catalog) and the WBD feature store, with documented feature contracts, backfill paths, and freshness SLAs.\nArchitect batch and near-real-time inference pipelines integrated with Snowflake and activation systems (Mosaic, FreeWheel, GAM).\nModeling & Experimentation\nDevelop and optimize models across the ML spectrum: gradient boosting (XGBoost/LightGBM), embedding/two-tower retrieval, neural ranking, probability calibration (e.g., isotonic regression), and probabilistic/graph-based matching.\nDesign rigorous offline and online experiments; define evaluation frameworks (precision/recall, AUC-ROC, NDCG, decile lift, calibration curves) appropriate to each use case.\nApply causal-inference techniques (propensity scoring, uplift/incrementality modeling) to measure true lift of audience targeting on engagement and retention KPIs.\nContribute to lookalike modeling (LAL 2.0+) using 1,000+ first- and third-party features, including privacy-safe builds inside Data Clean Rooms (Snowflake DCR).\n MLOps & Infrastructure\nChampion MLOps best practices: model versioning, champion/challenger promotion, automated retraining triggers, drift detection, and production monitoring with MLflow on Databricks.\nBuild and maintain robust, reproducible, auditable ML pipelines on Databricks (and AWS SageMaker where appropriate, e.g., the identity-resolution track); enforce leakage prevention and training/serving consistency.\nContribute to the team’s feature-store strategy - feature contracts, backfills, and freshness SLAs - and implement data-quality checks, model-health dashboards, and alerting thresholds.\nEmbed FinOps cost discipline (compute caps, auto-termination, job tagging) into pipeline design.\nMentorship & Cross-functional Collaboration\nMentor MLE 2s through code reviews, design discussions, and pairing; contribute to team technical standards.\nPartner with Product, Marketing, and Ad Sales to translate business requirements into ML problem formulations, and with Data Engineering on data contracts and pipeline SLAs.\nCommunicate model performance, trade-offs, and business impact clearly to technical and non-technical stakeholders.\nFlagship Projects You’ll Work On\n Identity Intelligence - foundational, privacy-safe identity across all WBD brands: probabilistic ID resolution that resolves unauthenticated signals to households/persons with calibrated confidence (entity resolution with gradient boosting and embeddings, representation learning, isotonic calibration, candidate blocking, champion/challenger pipelines), expanding addressable audiences beyond deterministic matching.\n Audience Intelligence - advertising and marketing use cases: lookalike and predictive audiences (LAL across 1,000+ features), ML-driven smart audiences, layered retrieval + propensity, and incrementality/closed-loop optimization, with privacy-safe activation including data clean rooms.\nML-based Forecasting - audience growth, demand, and advertising yield/pricing forecasting that powers ad sales and marketing decisions.\n Content Preferences & Affinity - genre-preference, content-preference, and single-title affinity modeling (two-tower retrieval with semantic content embeddings) that ranks audiences for upcoming titles and powers cross-channel promotion.\nWhat You’ll Bring\nRequired\n5-8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.).\nDeep Python expertise and production-quality software engineering practices; production experience building and deploying ML at scale (millions+ of users/records).\nStrong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT, MLflow, Unity Catalog) and solid SQL/Snowflake experience for feature sourcing and model-output delivery.\nExperience with AWS ML services (SageMaker, S3, Lambda).\nStrong understanding of ML model evaluation, A/B testing, and statistical inference; knowledge in one or more of recommendations & ranking, identity resolution, embeddings/retrieval, causal/interpretable ML, forecasting, bandits, or optimization.\nDemonstrated ability to lead technical decisions and mentor engineers.\nBachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent experience).\nExcellent written and verbal communication, with the ability to advocate technical solutions to engineers, scientists, and product stakeholders.\nPreferred:\nRecommendation systems, personalization, identity resolution, or audience modeling in a media / streaming / ad-tech context.\nFamiliarity with Data Clean Room environments (Snowflake DCR, AWS Clean Rooms) and consent-aware activation.\nExperience with two-tower / retrieval architectures, probabilistic identity resolution (graph-based matching, entity resolution), and probability calibration.\nHands-on experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, MCP), Databricks Genie Space configuration, and Snowflake Cortex.\nExperience with feature stores (Databricks Feature Store, Tecton, Feast) and contributions to open source or ML publications.\nWhat We Offer:\nA Great Place to work\nEqual opportunity employer\nFast track growth opportunities\nHow We Get Things Done…\nThis 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.\nChampioning Inclusion at WBD\nWarner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. 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