Vp, Product Management- Agentforce Voice

Salesforce Salesforce · Enterprise · San Francisco, CA

VP of Product Management to lead the global Speech, Language, and Industry Model Platform powering Agentforce Voice. This role owns the complete speech and language stack, including S2S, STT/ASR, TTS, multilingual support, accent/dialect adaptation, language expansion, speech quality, industry-specific models, and related capabilities. The focus is on defining how Salesforce delivers accurate, natural, low-latency voice experiences across various deployment models and customer environments, including hosted and self-hosted solutions, APIs, model orchestration, hybrid model management, performance tuning, model evaluation, domain adaptation, and enterprise-scale operations. The role involves building and leading a product organization with regional and industry focus, aligning with cross-functional teams, and managing a high-performing team of product managers.

What you'd actually do

  1. Team Leadership: Build, lead, and mentor a high-performing team of product managers, fostering a collaborative operating model, clear ownership boundaries, and a shared roadmap of priorities.
  2. Speech, Language, and Industry Model Strategy: Own the global strategy and roadmap for the Speech & Language Platform that powers Agentforce Voice, including S2S, STT/ASR, TTS, multilingual and monolingual support, accent and dialect adaptation, industry-specific models, speech quality, model selection, and API capabilities.
  3. Global Language Expansion: Define Salesforce's worldwide language strategy, prioritize new languages, dialects, accents, and regional variants, and establish the quality bar and investment model required for global enterprise readiness.
  4. Industry-Specific Models: Define and deliver domain-adapted speech and language models for strategic industries such as healthcare, financial services, public sector, communications, retail, automotive, and other terminology-intensive or regulated verticals.
  5. Regional Product Leadership: Build regional product capability in geographies requiring deep linguistic, cultural, regulatory, or market-specific expertise, including Japan, EMEA, Southeast Asia, India, Latin America, and other priority markets.

Skills

Required

  • Product Management
  • Speech Technology (S2S, STT/ASR, TTS)
  • Multilingual Language Support
  • Model Strategy and Roadmap Development
  • Global Language Expansion Strategy
  • Industry-Specific Model Development
  • Regional Product Management
  • Platform and Deployment Strategy (Managed, Self-hosted, Hybrid)
  • AI Model Management (Selection, Benchmarking, Tuning, Orchestration, Lifecycle)
  • Performance and Quality Improvement (Accuracy, Latency, Scalability)
  • Evaluation Frameworks and Governance
  • Customer and Partner Engagement
  • Product Lifecycle Management
  • Cross-Functional Collaboration

Nice to have

  • Deep understanding of Generative AI
  • Experience with enterprise voice AI
  • Knowledge of regulated verticals (healthcare, financial services, public sector)
  • Experience with APIs and inference infrastructure
  • Familiarity with Salesforce ecosystem

What the JD emphasized

  • deeply technical
  • transformative power of Generative AI
  • vision and operating discipline
  • mobilize product and engineering teams
  • accelerate innovation
  • drive market leadership and customer success
  • enterprise voice AI
  • own the complete speech and language stack
  • accurate, natural, low-latency voice experiences
  • enterprise scale
  • world-class product organization
  • high-performing team
  • high-velocity approach to delivery
  • clear ownership boundaries
  • global strategy and roadmap
  • worldwide language strategy
  • quality bar and investment model
  • domain-adapted speech and language models
  • terminology-intensive or regulated verticals
  • deep linguistic, cultural, regulatory, or market-specific expertise
  • managed, self-hosted, private, and hybrid deployment models
  • enterprise administration
  • proprietary, open, and third-party speech models
  • benchmarking, tuning, adaptation, orchestration, and lifecycle management
  • continuously improve recognition accuracy, synthesis quality, latency, streaming performance, barge-in responsiveness, reliability, scalability, cost efficiency, and conversational naturalness
  • common evaluation frameworks, scorecards, test datasets, release criteria, and governance
  • safety, compliance, fairness, and regional readiness
  • validate priorities, deliver roadmap updates, solicit feedback, and identify new market opportunities
  • comprehensive product requirements, documentation, operating metrics, and launch plans
  • Cross-Functional Alignment

Other signals

  • Generative AI
  • voice AI
  • Speech-to-Speech
  • Speech-to-Text
  • Automatic Speech Recognition
  • Text-to-Speech
  • multilingual
  • model-tuning
  • language models
  • low-latency voice experiences
  • APIs
  • model orchestration
  • hybrid model management
  • performance tuning
  • model evaluation
  • domain adaptation
  • enterprise scale
  • regional and industry-focused team members
  • languages, regulations, terminology, cultural expectations, or acoustic environments
  • proprietary, open, and third-party speech models
  • benchmarking
  • tuning
  • adaptation
  • orchestration
  • lifecycle management
  • recognition accuracy
  • synthesis quality
  • latency
  • streaming performance
  • barge-in responsiveness
  • reliability
  • scalability
  • cost efficiency
  • conversational naturalness
  • common evaluation frameworks
  • scorecards
  • test datasets
  • release criteria
  • governance for language quality
  • accent performance
  • industry terminology
  • safety
  • compliance
  • fairness
  • regional readiness