Staff Product Manager -Tech

Role overview

Staff Product Manager -Tech at Warner Bros. Discovery

Some Important Details.

This role focuses on context-aware localization, including transcription, subtitling, translation, captioning, audio description and dubbing workflows, as well as content intelligence capabilities that improve localization quality through scene, character, dialogue, and metadata awareness.

Responsibilities

• Write product requirement documents (PRDs) and define detailed functional and technical specifications for AI-powered localization workflows, including translation, dubbing, captioning, subtitle generation, audio description, editorial review, quality evaluation, and workflow orchestration systems.
• Collaborate with AI/ML teams to operationalize context-aware localization systems utilizing large language models (LLMs), metadata enrichment, retrieval-based approaches, and multimodal content understanding.
• Drive cross-functional alignment and decision-making across AI/ML engineering, media supply chain, global content operations, human-in-the-loop (HITL) operations, editorial, legal, accessibility, and external vendor partners to remove blockers, manage dependencies, and ensure successful execution of localization programs.
• Work with engineering and system architects to define integration requirements across localization supply chain systems, order management platforms, editorial tools, content management systems, metadata services, and adjacent AI/ML platforms.
• Assist in evaluating the impact of feature changes across adjacent systems on localization workflows and AI/ML pipelines, working with partner teams to scope dependencies and downstream effects.
• Collaborate with stakeholders across content operations, localization teams, marketing, and cross-functional technology teams to gather requirements and incorporate feedback while effectively managing the uncertainty inherent in AI/ML development.
• Deliver scalable AI-powered localization capabilities that improve localization quality, editorial efficiency, accessibility outcomes, and operational throughput.
• Drive measurable improvements in localization quality, post-edit effort, consistency, accessibility coverage, and cost efficiency across global content catalogs.
• Help scale the organization's localization AI capabilities by translating emerging AI technologies into operationally viable products that deliver measurable business value.

Employment details

• Location: Bangalore [Karnātaka] [India]

Additional role details

• The ideal candidate is detail-oriented, comfortable working closely with engineering and program management, and experienced in defining requirements and driving delivery for AI/ML-powered systems and backend platforms.
• Partner with engineering and program managers to drive execution across sprint cycles, including backlog management, story writing, feature clarification, dependency management, and release planning.
• Define and support capabilities that leverage content context, including character identity, dialogue history, scene metadata, terminology, style guides, content metadata, and franchise-specific knowledge to improve localization quality and consistency.
• Support the evolution of localization workflows from asset-level processing to context-aware systems that leverage metadata, character relationships, dialogue history, content intelligence, and editorial feedback to improve quality and consistency at scale.
• Facilitate near-term roadmap planning and coordination across engineering and partner teams supporting localization and content intelligence initiatives.
• Partner with Product Data Science to define and evaluate experimentation frameworks, interpret results, and assess localization quality, model performance, post-edit efficiency, workflow productivity, and business impact to inform product decisions and prioritization.
• Partner with editorial and localization operations teams to design and optimize human-in-the-loop workflows, ensuring reviewer feedback mechanisms align with model evaluation, quality measurement, and continuous improvement objectives.
• Directly support product operations, including triaging user feedback, investigating product issues, and partnering with engineering and program management to prioritize fixes and enhancements. 9–12 years of product management experience, including experience as a Technical Product Manager.
• Strong experience writing PRDs, defining features, and partnering with engineering teams through agile development and sprint execution processes.
• Familiarity with machine learning systems, including deep learning and large language models, deployed in production environments.
• Experience working with AI/ML systems on cloud platforms such as AWS or GCP, particularly those involving human-in-the-loop workflows, content processing pipelines, or post-editing workflows.
• Strong attention to detail, with the ability to manage multiple concurrent feature tracks, dependencies, and decisions across complex systems.
• Experience translating business and operational requirements into technical product capabilities and measurable outcomes.
• Background in Computer Science, Engineering, Data Science, or a related technical field.
• Familiarity with language AI technologies and media localization workflows, including Automatic Speech Recognition (ASR), Machine Translation (MT), Large Language Models (LLMs), Text-to-Speech (TTS), dubbing, captioning, subtitling, and audio description.
• Experience with context-aware localization systems leveraging metadata, terminology management, style guides, character context, dialogue history, and retrieval-based approaches.
• Familiarity with localization quality evaluation methodologies, human-in-the-loop review processes, and post-editing workflows.
• Comfortable working with AI evaluation methodologies and quality metrics, including precision, recall, confidence scoring, translation quality assessment, and human evaluation frameworks.
• Advance context-aware localization workflows by incorporating content metadata, character context, dialogue history, and editorial feedback into AI-assisted processes.
• Successfully coordinate delivery across engineering, AI/ML, editorial, operations, legal, accessibility, and vendor teams while maintaining strong execution rigor and stakeholder alignment.
• Challenging ML problems in Media Domain.