{"schemaVersion":"jobsearcher.job.v1","id":"b71def5208f6343d0aef6a69","url":"https://jobsearcher.com/jobs/b71def5208f6343d0aef6a69","canonicalUrl":"https://jobsearcher.com/jobs/b71def5208f6343d0aef6a69","title":"Senior Machine Learning Engineer","description":"Welcome to Warner Bros. 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.\nAt HBO Max, storytelling takes center stage. We’re one of the world’s most iconic entertainment brands — home to bold originals and unforgettable characters. While audiences binge award-winning content, breaking news, and sports around the clock, our teams stay busy at work creating what’s next in streaming. From Succession, Euphoria, and The Sopranos to global franchises like Game of Thrones and Harry Potter, our content sparks conversation and shapes culture.\nHBO Max delivers boundary-pushing stories across genres and platforms, connecting millions of viewers across 90 countries globally— and we’re just getting started. We're home to the most talked about shows and movies, granting audiences access to the worlds of HBO, Harry Potter, DC, Warner Bros., ID, Adult Swim, A24, and more. Turn your streaming obsession into a career— we’re hiring!\nSenior Machine Learning Engineer\nTeam: Data & Audience Platform (DAP) — ML Engineering\n\nWhat We Do\nWarner Bros. Discovery (WBD) is home to the world’s most iconic entertainment, news, and sports brands — HBO Max, CNN, Discovery+, DC, Warner Bros.,\nBleacher Report, Food Network, and many more. Within the Data & Audience\nPlatform (DAP) organization, our Machine Learning Engineering team builds the\nfoundational AI/ML intelligence that powers identity, audience, advertising, and\npersonalization across every WBD brand. We turn first-party signals from\nhundreds of millions of viewers into production ML systems that expand\naddressable audiences, sharpen targeting and measurement, forecast demand,\nand personalize content discovery — directly driving advertising yield, marketing\nefficiency, engagement, and retention.\nAt WBD, Machine Learning Engineering does rigorous data science and own the\nengineering that brings models to life: production ML data pipelines, model\ntraining and optimization, and the ML infrastructure — feature stores, training\nand serving pipelines, and MLOps — that makes our work reliable, repeatable,\nand scalable. We build primarily on Databricks, with strong working knowledge\nof Snowflake and AWS, and we are an early, enthusiastic adopter of agentic AI\ndevelopment workflows.\n\nAbout the Role:\nThis is a senior, high-ownership US-based role that sits between our Senior MLE\nand Staff MLE levels. You will own the design and delivery of production ML\nsystems end to end and take on cross-cutting technical leadership: setting\npatterns, driving key architectural decisions on flagship workstreams, and raising the bar for the broader ML organization — including close partnership with our Hyderabad ML team. As a US-based senior engineer, you will also serve as a\ntechnical anchor and time-zone bridge across the global team: framing\nambiguous problems, unblocking others, and translating business priorities from US-based Product, Marketing, and Ad Sales stakeholders into an executable ML roadmap.\n\nThis role is ideal for engineers with roughly 5–8 years of experience (3+ with a\nPhD) who operate with strong autonomy, lead by influence, and can move fluidly\nfrom hands-on modeling and pipeline engineering to architecture and\nmentorship. You will do meaningful individual technical work while beginning to\nexercise Staff-level scope across initiatives.\n\nWhat You’ll Do:\nML System Design & Technical Leadership\nLead end-to-end development of production ML systems: data sourcing,\nfeature engineering, model training, evaluation, deployment, and\nmonitoring.\nOwn one or more flagship ML products — e.g., probabilistic identity\nresolution (matching unauthenticated device IDs and 1P cookies to\nhouseholds/persons with calibrated confidence), single-title affinity (two-\ntower retrieval), lookalike modeling, or forecasting — and drive their\ntechnical direction.\nMake and document key architectural decisions across a workstream\n(feature-store design, training/serving patterns, evaluation frameworks);\nprovide deep trade-off analysis on scalability, latency, reliability, and cost.\nDesign scalable feature and inference pipelines on Databricks (PySpark,\nDelta, Workflows/DLT, Unity Catalog) integrated with Snowflake and\nactivation systems (Mosaic, FreeWheel, GAM), with documented feature\ncontracts, backfill paths, and freshness SLAs.\nEstablish and evangelize patterns that other engineers adopt; anticipate\nrisks and failure modes before they surface.\n\nModeling & Experimentation\nDevelop and optimize models across the ML spectrum: gradient boosting\n(XGBoost/LightGBM), embedding/two-tower retrieval, neural ranking,\nprobability calibration (e.g., isotonic regression), and probabilistic/graph-\nbased matching.\nDesign rigorous offline and online experiments; define evaluation\nframeworks (precision/recall, AUC-ROC, NDCG, decile lift, calibration\ncurves) appropriate to each use case.\nApply causal-inference techniques (propensity scoring,\nuplift/incrementality modeling) to measure true lift of audience targeting\non engagement and retention KPIs.\nContribute to lookalike modeling (LAL 2.0+) using 1,000+ first- and third-\nparty features, including privacy-safe builds inside Data Clean Rooms\n(Snowflake DCR).\nMLOps & Infrastructure\nChampion MLOps best practices: model versioning, champion/challenger\npromotion, automated retraining triggers, drift detection, and production\nmonitoring with MLflow on Databricks.\nBuild and maintain robust, reproducible, auditable ML pipelines on\nDatabricks (and AWS SageMaker where appropriate, e.g., the identity-\nresolution track); enforce leakage prevention and training/serving\nconsistency.\nShape the team’s feature-store strategy — feature contracts, backfills, and\nfreshness SLAs — and implement data-quality checks, model-health\ndashboards, and alerting thresholds.\nEmbed FinOps cost discipline (compute caps, auto-termination, job tagging)\ninto pipeline design.\nAgentic AI & Modern Development\nActively use and advocate for AI-assisted development: Cursor, GitHub\nCopilot, and Amazon Q for code generation, review, and documentation.\nLeverage Databricks Genie as a governed natural-language analytics layer\n— configuring Genie Spaces over ML feature tables and audience datasets\nto enable self-service exploration for cross-functional stakeholders.\nUse Snowflake Cortex (Copilot, Cortex Analyst, Cortex Search) to\naccelerate SQL authoring, data discovery, and RAG-based internal tooling\nover Snowflake-resident identity and audience data.\nDesign and prototype agentic ML workflows (MCP-compatible tooling,\nLangChain/LangGraph) to automate repetitive tasks such as data\nvalidation, feature selection, and hyperparameter search; evaluate LLM-\nbased approaches for metadata enrichment and content understanding.\nMentorship & Cross-functional Collaboration\nMentor Senior and MLE 2 engineers — including members of the\nHyderabad team — through code reviews, design discussions, and pairing;\ncontribute to and help set team technical standards.\nServe as a US-based point of contact and time-zone bridge for the global ML\nteam; help align priorities and unblock the India team across time zones.\nPartner with US-based Product, Marketing, and Ad Sales stakeholders to\ntranslate business requirements into ML problem formulations, and with\nData Engineering on data contracts and pipeline SLAs.\nCommunicate model performance, trade-offs, and business impact clearly\nto technical and non-technical stakeholders.\nFlagship Projects You’ll Work On\nIdentity Intelligence — foundational, privacy-safe identity across all WBD\nbrands: probabilistic ID resolution that resolves unauthenticated signals to\nhouseholds/persons with calibrated confidence (entity resolution with\ngradient boosting and embeddings, representation learning, isotonic\ncalibration, candidate blocking, champion/challenger pipelines), expanding\naddressable audiences beyond deterministic matching.\nAudience Intelligence — advertising and marketing use cases: lookalike\nand predictive audiences (LAL across 1,000+ features), ML-driven smart\naudiences, layered retrieval + propensity, and incrementality/closed-loop\noptimization, with privacy-safe activation including data clean rooms.\nML-based Forecasting — audience growth, demand, and advertising\nyield/pricing forecasting that powers ad sales and marketing decisions.\nContent Preferences & Affinity — genre-preference, content-preference,\nand single-title affinity modeling (two-tower retrieval with semantic\ncontent embeddings) that ranks audiences for upcoming titles and powers\ncross-channel promotion.\n\nWhat You’ll Bring:\nRequired\n5–8 years of industry experience in ML engineering or applied data science\n(3+ years with a Ph.D.), including a track record of leading projects to\nproduction.\nDeep Python expertise and strong software engineering practices;\nproduction experience building and deploying ML at scale (millions+ of\nusers/records).\nStrong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT,\nMLflow, Unity Catalog) and solid SQL/Snowflake experience for feature\nsourcing and model-output delivery.\nExperience with AWS ML services (SageMaker, S3, Lambda).\nStrong understanding of ML model evaluation, A/B testing, and\nstatistical/causal inference; depth in one or more of recommendations &\nranking, identity resolution, embeddings/retrieval, forecasting, or\noptimization.\nDemonstrated technical leadership: driving architectural decisions, setting\npatterns/standards, and mentoring other engineers — including leading by\ninfluence across teams and time zones.\nBachelor’s or Master’s degree in Computer Science, Statistics, Engineering,\nor a related quantitative field (or equivalent experience).\nExcellent written and verbal communication, with the ability to advocate\ntechnical solutions to engineers, scientists, and product stakeholders.\n\nPreferred:\nRecommendation systems, personalization, identity resolution, or audience\nmodeling in a media / streaming / ad-tech context.\nExperience with two-tower / retrieval architectures, probabilistic identity\nresolution (graph-based matching, entity resolution, confidence\ncalibration), and Data Clean Room ML (Snowflake DCR, AWS Clean Rooms).\nExperience architecting or standardizing components of an ML platform\nused by multiple engineers or teams.\nHands-on experience with agentic AI frameworks (LangChain, LangGraph,\nAutoGen, MCP), Databricks Genie Space configuration, and Snowflake\nCortex.\nExperience with feature stores (Databricks Feature Store, Tecton, Feast)\nand contributions to open source or ML publications.\nExperience partnering with or mentoring globally distributed teams.\nOur Technology Stack\nPrimary platform: Databricks (Lakehouse, PySpark, Delta, Workflows/DLT,\nMLflow, Feature Store, Unity Catalog, Asset Bundles, Genie). Cloud: AWS\n(SageMaker, S3, Lambda). Warehouse: Snowflake (incl. DCR, Snowpark, Cortex).\nActivation: Mosaic, FreeWheel, Google Ad Manager. Agentic AI: Cursor, GitHub\nCopilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP. Languages: Python\n(primary), SQL, Scala (as needed).\n\nWarner Bros. Discovery is an equal opportunity employer. We celebrate diversity\nand are committed to creating an inclusive environment for all employees.\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. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity or expression, age, mental or physical disability, and genetic information, marital status, citizenship status, military status, protected veteran status or any other category protected by law.\nIf 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.\nIn compliance with local law, we are disclosing the compensation, or a range thereof, for roles in locations where legally required. Actual salaries will vary based on several factors, including but not limited to external market data, internal equity, location, skill set, experience, and/or performance. Base pay is just one component of Warner Bros. Discovery’s total compensation package for employees. Pay Range: $159,180.00 - $295,620.00 salary per year. Other rewards may include annual bonuses, short- and long-term incentives, and program-specific awards. In addition, Warner Bros. Discovery provides a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, a retirement savings plan, paid holidays and sick time and vacation.\nIf you’re a qualified candidate with an arrest or conviction record, please know that your application will be considered in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.","company":"Warnerbrosdiscovery","rawCompany":"warnerbrosdiscovery","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-30T11:33:08.478Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"516210","title":"Media Streaming Distribution Services, Social Networks, and Other Media Networks and Content Providers","slug":"media-streaming-distribution-services-social-networks-and-other-media-networks-and-content-providers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Machine Learning Engineer","description":"Welcome to Warner Bros. 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.\nAt HBO Max, storytelling takes center stage. We’re one of the world’s most iconic entertainment brands — home to bold originals and unforgettable characters. While audiences binge award-winning content, breaking news, and sports around the clock, our teams stay busy at work creating what’s next in streaming. From Succession, Euphoria, and The Sopranos to global franchises like Game of Thrones and Harry Potter, our content sparks conversation and shapes culture.\nHBO Max delivers boundary-pushing stories across genres and platforms, connecting millions of viewers across 90 countries globally— and we’re just getting started. We're home to the most talked about shows and movies, granting audiences access to the worlds of HBO, Harry Potter, DC, Warner Bros., ID, Adult Swim, A24, and more. Turn your streaming obsession into a career— we’re hiring!\nSenior Machine Learning Engineer\nTeam: Data & Audience Platform (DAP) — ML Engineering\n\nWhat We Do\nWarner Bros. Discovery (WBD) is home to the world’s most iconic entertainment, news, and sports brands — HBO Max, CNN, Discovery+, DC, Warner Bros.,\nBleacher Report, Food Network, and many more. Within the Data & Audience\nPlatform (DAP) organization, our Machine Learning Engineering team builds the\nfoundational AI/ML intelligence that powers identity, audience, advertising, and\npersonalization across every WBD brand. We turn first-party signals from\nhundreds of millions of viewers into production ML systems that expand\naddressable audiences, sharpen targeting and measurement, forecast demand,\nand personalize content discovery — directly driving advertising yield, marketing\nefficiency, engagement, and retention.\nAt WBD, Machine Learning Engineering does rigorous data science and own the\nengineering that brings models to life: production ML data pipelines, model\ntraining and optimization, and the ML infrastructure — feature stores, training\nand serving pipelines, and MLOps — that makes our work reliable, repeatable,\nand scalable. We build primarily on Databricks, with strong working knowledge\nof Snowflake and AWS, and we are an early, enthusiastic adopter of agentic AI\ndevelopment workflows.\n\nAbout the Role:\nThis is a senior, high-ownership US-based role that sits between our Senior MLE\nand Staff MLE levels. You will own the design and delivery of production ML\nsystems end to end and take on cross-cutting technical leadership: setting\npatterns, driving key architectural decisions on flagship workstreams, and raising the bar for the broader ML organization — including close partnership with our Hyderabad ML team. As a US-based senior engineer, you will also serve as a\ntechnical anchor and time-zone bridge across the global team: framing\nambiguous problems, unblocking others, and translating business priorities from US-based Product, Marketing, and Ad Sales stakeholders into an executable ML roadmap.\n\nThis role is ideal for engineers with roughly 5–8 years of experience (3+ with a\nPhD) who operate with strong autonomy, lead by influence, and can move fluidly\nfrom hands-on modeling and pipeline engineering to architecture and\nmentorship. You will do meaningful individual technical work while beginning to\nexercise Staff-level scope across initiatives.\n\nWhat You’ll Do:\nML System Design & Technical Leadership\nLead end-to-end development of production ML systems: data sourcing,\nfeature engineering, model training, evaluation, deployment, and\nmonitoring.\nOwn one or more flagship ML products — e.g., probabilistic identity\nresolution (matching unauthenticated device IDs and 1P cookies to\nhouseholds/persons with calibrated confidence), single-title affinity (two-\ntower retrieval), lookalike modeling, or forecasting — and drive their\ntechnical direction.\nMake and document key architectural decisions across a workstream\n(feature-store design, training/serving patterns, evaluation frameworks);\nprovide deep trade-off analysis on scalability, latency, reliability, and cost.\nDesign scalable feature and inference pipelines on Databricks (PySpark,\nDelta, Workflows/DLT, Unity Catalog) integrated with Snowflake and\nactivation systems (Mosaic, FreeWheel, GAM), with documented feature\ncontracts, backfill paths, and freshness SLAs.\nEstablish and evangelize patterns that other engineers adopt; anticipate\nrisks and failure modes before they surface.\n\nModeling & Experimentation\nDevelop and optimize models across the ML spectrum: gradient boosting\n(XGBoost/LightGBM), embedding/two-tower retrieval, neural ranking,\nprobability calibration (e.g., isotonic regression), and probabilistic/graph-\nbased matching.\nDesign rigorous offline and online experiments; define evaluation\nframeworks (precision/recall, AUC-ROC, NDCG, decile lift, calibration\ncurves) appropriate to each use case.\nApply causal-inference techniques (propensity scoring,\nuplift/incrementality modeling) to measure true lift of audience targeting\non engagement and retention KPIs.\nContribute to lookalike modeling (LAL 2.0+) using 1,000+ first- and third-\nparty features, including privacy-safe builds inside Data Clean Rooms\n(Snowflake DCR).\nMLOps & Infrastructure\nChampion MLOps best practices: model versioning, champion/challenger\npromotion, automated retraining triggers, drift detection, and production\nmonitoring with MLflow on Databricks.\nBuild and maintain robust, reproducible, auditable ML pipelines on\nDatabricks (and AWS SageMaker where appropriate, e.g., the identity-\nresolution track); enforce leakage prevention and training/serving\nconsistency.\nShape the team’s feature-store strategy — feature contracts, backfills, and\nfreshness SLAs — and implement data-quality checks, model-health\ndashboards, and alerting thresholds.\nEmbed FinOps cost discipline (compute caps, auto-termination, job tagging)\ninto pipeline design.\nAgentic AI & Modern Development\nActively use and advocate for AI-assisted development: Cursor, GitHub\nCopilot, and Amazon Q for code generation, review, and documentation.\nLeverage Databricks Genie as a governed natural-language analytics layer\n— configuring Genie Spaces over ML feature tables and audience datasets\nto enable self-service exploration for cross-functional stakeholders.\nUse Snowflake Cortex (Copilot, Cortex Analyst, Cortex Search) to\naccelerate SQL authoring, data discovery, and RAG-based internal tooling\nover Snowflake-resident identity and audience data.\nDesign and prototype agentic ML workflows (MCP-compatible tooling,\nLangChain/LangGraph) to automate repetitive tasks such as data\nvalidation, feature selection, and hyperparameter search; evaluate LLM-\nbased approaches for metadata enrichment and content understanding.\nMentorship & Cross-functional Collaboration\nMentor Senior and MLE 2 engineers — including members of the\nHyderabad team — through code reviews, design discussions, and pairing;\ncontribute to and help set team technical standards.\nServe as a US-based point of contact and time-zone bridge for the global ML\nteam; help align priorities and unblock the India team across time zones.\nPartner with US-based Product, Marketing, and Ad Sales stakeholders to\ntranslate business requirements into ML problem formulations, and with\nData Engineering on data contracts and pipeline SLAs.\nCommunicate model performance, trade-offs, and business impact clearly\nto technical and non-technical stakeholders.\nFlagship Projects You’ll Work On\nIdentity Intelligence — foundational, privacy-safe identity across all WBD\nbrands: probabilistic ID resolution that resolves unauthenticated signals to\nhouseholds/persons with calibrated confidence (entity resolution with\ngradient boosting and embeddings, representation learning, isotonic\ncalibration, candidate blocking, champion/challenger pipelines), expanding\naddressable audiences beyond deterministic matching.\nAudience Intelligence — advertising and marketing use cases: lookalike\nand predictive audiences (LAL across 1,000+ features), ML-driven smart\naudiences, layered retrieval + propensity, and incrementality/closed-loop\noptimization, with privacy-safe activation including data clean rooms.\nML-based Forecasting — audience growth, demand, and advertising\nyield/pricing forecasting that powers ad sales and marketing decisions.\nContent Preferences & Affinity — genre-preference, content-preference,\nand single-title affinity modeling (two-tower retrieval with semantic\ncontent embeddings) that ranks audiences for upcoming titles and powers\ncross-channel promotion.\n\nWhat You’ll Bring:\nRequired\n5–8 years of industry experience in ML engineering or applied data science\n(3+ years with a Ph.D.), including a track record of leading projects to\nproduction.\nDeep Python expertise and strong software engineering practices;\nproduction experience building and deploying ML at scale (millions+ of\nusers/records).\nStrong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT,\nMLflow, Unity Catalog) and solid SQL/Snowflake experience for feature\nsourcing and model-output delivery.\nExperience with AWS ML services (SageMaker, S3, Lambda).\nStrong understanding of ML model evaluation, A/B testing, and\nstatistical/causal inference; depth in one or more of recommendations &\nranking, identity resolution, embeddings/retrieval, forecasting, or\noptimization.\nDemonstrated technical leadership: driving architectural decisions, setting\npatterns/standards, and mentoring other engineers — including leading by\ninfluence across teams and time zones.\nBachelor’s or Master’s degree in Computer Science, Statistics, Engineering,\nor a related quantitative field (or equivalent experience).\nExcellent written and verbal communication, with the ability to advocate\ntechnical solutions to engineers, scientists, and product stakeholders.\n\nPreferred:\nRecommendation systems, personalization, identity resolution, or audience\nmodeling in a media / streaming / ad-tech context.\nExperience with two-tower / retrieval architectures, probabilistic identity\nresolution (graph-based matching, entity resolution, confidence\ncalibration), and Data Clean Room ML (Snowflake DCR, AWS Clean Rooms).\nExperience architecting or standardizing components of an ML platform\nused by multiple engineers or teams.\nHands-on experience with agentic AI frameworks (LangChain, LangGraph,\nAutoGen, MCP), Databricks Genie Space configuration, and Snowflake\nCortex.\nExperience with feature stores (Databricks Feature Store, Tecton, Feast)\nand contributions to open source or ML publications.\nExperience partnering with or mentoring globally distributed teams.\nOur Technology Stack\nPrimary platform: Databricks (Lakehouse, PySpark, Delta, Workflows/DLT,\nMLflow, Feature Store, Unity Catalog, Asset Bundles, Genie). Cloud: AWS\n(SageMaker, S3, Lambda). Warehouse: Snowflake (incl. DCR, Snowpark, Cortex).\nActivation: Mosaic, FreeWheel, Google Ad Manager. Agentic AI: Cursor, GitHub\nCopilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP. Languages: Python\n(primary), SQL, Scala (as needed).\n\nWarner Bros. Discovery is an equal opportunity employer. We celebrate diversity\nand are committed to creating an inclusive environment for all employees.\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. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity or expression, age, mental or physical disability, and genetic information, marital status, citizenship status, military status, protected veteran status or any other category protected by law.\nIf 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.\nIn compliance with local law, we are disclosing the compensation, or a range thereof, for roles in locations where legally required. Actual salaries will vary based on several factors, including but not limited to external market data, internal equity, location, skill set, experience, and/or performance. Base pay is just one component of Warner Bros. Discovery’s total compensation package for employees. Pay Range: $159,180.00 - $295,620.00 salary per year. Other rewards may include annual bonuses, short- and long-term incentives, and program-specific awards. In addition, Warner Bros. Discovery provides a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, a retirement savings plan, paid holidays and sick time and vacation.\nIf you’re a qualified candidate with an arrest or conviction record, please know that your application will be considered in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.","datePosted":"2026-07-30T11:33:08.478Z","dateModified":"2026-07-30T11:33:08.478Z","hiringOrganization":{"@type":"Organization","name":"Warnerbrosdiscovery","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b71def5208f6343d0aef6a69"},"url":"https://jobsearcher.com/jobs/b71def5208f6343d0aef6a69"}}