New opportunity

Staff Product Manager -Tech

Warner Bros. Discovery · Bangalore, India

About this role

The Content Intelligence team at Warner Bros. Discovery is seeking a Staff-level Technical Product Manager (PM-T) to lead the development and execution of AI/ML capabilities for next-generation content localization workflows. This role focuses on context-aware localization, including transcription, subtitling, translation, captioning, audio description, and dubbing. You will drive the operationalization of AI/ML systems to improve global content reach and accessibility. Key Responsibilities: Partner with engineering and program managers to drive execution across sprint cycles, including backlog management and release planning. Write detailed PRDs and functional specifications for AI-powered localization workflows. Define capabilities leveraging content context such as character identity, dialogue history, and scene metadata. Collaborate with AI/ML teams to operationalize systems utilizing LLMs, retrieval-based approaches, and multimodal content understanding. Drive cross-functional alignment across AI/ML engineering, media supply chain, and human-in-the-loop operations. Partner with Product Data Science to define experimentation frameworks and evaluate model performance and workflow productivity. Required Qualifications: 9–12 years of product management experience, including experience as a Technical Product Manager. Strong experience writing PRDs and partnering with engineering teams in agile environments. Familiarity with machine learning systems, including deep learning and LLMs, in production. Experience with cloud platforms (AWS or GCP) and human-in-the-loop workflows. Preferred Qualifications: Background in Computer Science, Engineering, or Data Science. Familiarity with language AI technologies (ASR, MT, TTS) and localization quality evaluation methodologies. Success in this role involves delivering scalable AI-powered localization capabilities that improve quality, editorial efficiency, and cost-effectiveness across global content catalogs.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product ManagementTechnical Product ManagementAI/MLLarge Language ModelsDeep LearningContent LocalizationAgilePRD WritingCloud PlatformsAWSGCPHuman-in-the-loopAutomatic Speech RecognitionMachine TranslationText-to-SpeechData ScienceMetadata Management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Operationalizing AI/ML for Localization
The primary objective is to transition localization workflows from basic asset processing to advanced, context-aware systems. This requires deep collaboration with AI/ML engineering to integrate LLMs, retrieval-based approaches, and multimodal content understanding into production pipelines for transcription, dubbing, and subtitling.
Defining Context-Aware Product Requirements
You must translate complex business needs into detailed PRDs that incorporate content metadata, character identity, dialogue history, and style guides. Success depends on your ability to specify how these context signals improve the quality and consistency of automated localization outputs.
Optimizing Human-in-the-Loop (HITL) Workflows
A critical duty is designing feedback mechanisms that bridge the gap between automated AI outputs and editorial review. You will partner with operations teams to ensure that human feedback is systematically captured to refine model performance and improve post-edit efficiency.
Cross-Functional Stakeholder Alignment
You are responsible for driving alignment across diverse groups including media supply chain, legal, accessibility, and external vendors. You must manage dependencies and resolve blockers to ensure that technical AI initiatives meet the operational requirements of global content distribution.

Likely challenges to think through

Managing Uncertainty in AI Development
AI/ML projects often face unpredictable performance outcomes. To prepare, demonstrate your experience in defining experimentation frameworks and quality metrics (e.g., precision, recall, translation quality assessment) to objectively measure progress and pivot when models underperform.
Balancing Automation with Editorial Quality
There is an inherent trade-off between scaling localization through AI and maintaining the high editorial standards required for global media. Prepare by discussing how you have previously balanced cost-effective automation with rigorous quality evaluation methodologies to satisfy both operational and creative stakeholders.
Integrating Complex Legacy Systems
The role requires integrating new AI capabilities into existing media supply chain and content management systems. Prepare by highlighting your experience in scoping technical dependencies and managing the downstream effects of feature changes on established, mission-critical production pipelines.

Practice your answers

Technical practice

Designing Context-Aware Localization Workflows

What to coverExplain how you would integrate metadata, character identity, and dialogue history into an AI-driven localization pipeline. Discuss the trade-offs between using retrieval-augmented generation (RAG) versus fine-tuning models to maintain consistency across large content catalogs.

Operationalizing LLMs in Production

What to coverDescribe your approach to deploying LLMs for tasks like subtitling or dubbing while ensuring reliability. Address how you manage latency, cost, and model drift in a high-volume media supply chain environment.

Human-in-the-Loop (HITL) Strategy

What to coverHow do you design HITL workflows to optimize for both editorial quality and operational efficiency? Suggest metrics to measure the effectiveness of human feedback loops in improving model performance over time.

Defining AI/ML Experimentation Frameworks

What to coverWhen partnering with Data Science, what key performance indicators (KPIs) would you prioritize to evaluate the success of a new AI-powered localization feature? Explain how you balance technical precision metrics with business-level outcomes like post-edit effort.

Managing Technical Debt in AI Systems

What to coverIn a fast-paced agile environment, how do you balance the need for rapid feature delivery with the necessity of maintaining robust, scalable AI infrastructure? Discuss your process for prioritizing technical debt versus new product capabilities.

Integration Across Media Supply Chain Systems

What to coverDescribe the challenges of integrating AI/ML pipelines with existing content management and order management systems. How do you ensure data consistency and seamless hand-offs between disparate platforms?

Quality Evaluation Methodologies

What to coverWhat methodologies would you implement to assess the quality of automated translations or transcriptions? Discuss the role of automated scoring versus human evaluation in your quality assurance strategy.

Handling Uncertainty in AI Development

What to coverAI projects often involve unpredictable outcomes. Explain your strategy for managing stakeholder expectations and adjusting roadmaps when model performance does not meet initial requirements.

Behavioural practice

Cross-Functional Alignment on Complex Initiatives

What to coverDescribe a situation where you had to align diverse stakeholders—such as engineering, legal, and editorial—on a controversial or technically complex product decision. How did you manage competing priorities to reach a consensus?

Driving Execution Through Ambiguity

What to coverProvide an example of a time you led a project with significant technical uncertainty. How did you break down the problem, manage dependencies, and ensure the team remained focused on delivering measurable value?

Influencing Without Direct Authority

What to coverTell me about a time you had to influence a partner team or vendor to adopt a new process or technology. What steps did you take to build trust and demonstrate the business impact of your proposal?

Managing Product Operations and Feedback

What to coverDescribe a time you had to pivot your product strategy based on user feedback or operational issues. How did you investigate the root cause and communicate the necessary changes to your engineering team?

Sharpen your knowledge

Topics to prepare

Context-Aware Localization Systems
Review the conceptual framework of integrating metadata, character identity, and dialogue history into AI localization pipelines. Exercise: Map out how a character's specific style guide or relationship history would influence a machine translation output for a scene. This is directly relevant to the role's focus on improving localization quality through context.
Human-in-the-Loop (HITL) Evaluation Frameworks
Study methodologies for balancing automated AI outputs with human editorial review. Exercise: Design a feedback loop that captures post-edit effort metrics to refine model performance. This is essential for the role's responsibility to optimize editorial efficiency and quality evaluation.

Skills to revise

Technical PRD Writing for AI/ML
Practice drafting a Product Requirement Document that specifies functional requirements for an AI-powered workflow, including edge cases and performance metrics. This is a core requirement for driving execution in the Content Intelligence team.
AI/ML Model Performance Metrics
Familiarize yourself with evaluating language AI technologies using metrics like precision, recall, and confidence scoring. Exercise: Define success criteria for an ASR or MT model in a production media environment. This is critical for partnering with Product Data Science to assess workflow productivity.
Agile Backlog Management
Refine techniques for managing complex backlogs involving cross-functional dependencies between engineering, AI/ML, and operations teams. Exercise: Create a prioritized sprint plan that balances new feature development with technical debt reduction in a media supply chain context.

Your preparation checklist

Analyze AI-driven localization workflows
Prepare a brief overview of how LLMs and retrieval-based systems can improve content localization, specifically focusing on how metadata, character identity, and dialogue history can be integrated into automated transcription, dubbing, and subtitling pipelines.
Draft a sample PRD for a localization feature
Create a mock Product Requirement Document for an AI-powered feature, such as an automated subtitle quality evaluation tool. Include functional specifications, success metrics like post-edit efficiency, and integration requirements with existing media supply chain systems.
Develop a framework for human-in-the-loop (HITL) evaluation
Outline a strategy for balancing AI automation with human editorial review. Define how you would measure model performance versus human feedback to ensure high-quality output in a production environment.
Prepare examples of cross-functional stakeholder management
Be ready to discuss specific instances where you aligned engineering, data science, and operational teams to resolve technical blockers or manage dependencies in a complex, high-stakes product environment.
Formulate strategic questions for the hiring team
Prepare insightful questions regarding the current maturity of their AI/ML localization stack, the biggest bottlenecks in their existing human-in-the-loop workflows, and how they prioritize technical debt versus new feature development.

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