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Zuora

SaaS platform for subscription lifecycle management

Vice President of Product for AI Platform

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeFoster City, CA, USAIn-person

About the Job

Requirements

  • Significant experience building and scaling platforms, infrastructure, or distributed systems in complex enterprise software environments, with a strong understanding of modern AI architectures and production-grade AI infrastructure.
  • Familiarity with large language models, model serving and orchestration, retrieval-augmented generation, embeddings, vector search, context engineering, and agentic application architectures.
  • Experience designing systems that integrate AI capabilities with enterprise data, APIs, business logic, and transactional systems.
  • Strong grounding in cloud architecture, distributed systems, security, reliability, and scalable software engineering.
  • Ability to distinguish durable architectural principles from rapidly changing AI capabilities.
  • Ability to identify foundational platform services versus capabilities specific to individual products or use cases.
  • Understanding of context, permissions, data access, evaluation, observability, and reliability requirements for enterprise AI workflows, and a practical perspective on the limitations and changing nature of current AI systems.
  • Experience taking complex technical initiatives from concept through architecture, development, production deployment, and scaled adoption.
  • Ability to engage deeply in technical decisions, challenge assumptions, and make trade-offs among speed, flexibility, cost, reliability, and long-term architectural integrity.
  • Ability to determine when to build, buy, partner, or wait for technology to mature.
  • Understanding of how platform architecture and investment decisions connect to customer value, product differentiation, developer productivity, and commercial impact.
  • Ability to work with senior Product and Engineering leaders to identify high-value AI opportunities and prioritize simplicity, reuse, adoption, and measurable outcomes.
  • Experience building technology that meets demanding security, privacy, reliability, and enterprise-scale operational requirements.
  • Ability to establish standards and governance without unnecessary bureaucracy or slowing innovation.
  • Ability to communicate complex technical concepts to technical teams, product leaders, and executive stakeholders, and influence across organizational boundaries to build alignment around architecture, priorities, and shared ownership.
  • Ability to attract strong technical talent and create an environment where high-performing engineers can do their best work.
  • Intellectual curiosity, adaptability, and sound judgment in evaluating technical opportunities amid uncertainty.

Responsibilities

  • Define the vision, strategy, and technical architecture for Zuora’s AI platform in alignment with its broader AI-native transformation.
  • Determine the foundational capabilities needed to support AI-powered products, intelligent workflows, and agentic applications across the portfolio.
  • Establish a coherent platform architecture that enables reuse, interoperability, extensibility, and rapid innovation across product teams.
  • Make build-versus-buy decisions, leveraging external models, services, and infrastructure where appropriate while investing in strategically differentiating capabilities.
  • Establish a roadmap balancing near-term product opportunities with long-term platform capabilities.
  • Develop shared capabilities that enable product teams to incorporate AI into customer experiences and business workflows without rebuilding foundational infrastructure for each use case.
  • Establish technical foundations for AI agents to understand user intent, access relevant context, reason through tasks, interact with enterprise systems, and take appropriate action.
  • Define approaches to model integration and orchestration, context management, retrieval-augmented generation, tool use, and agent execution.
  • Enable secure and reliable access to enterprise data, business logic, APIs, and transactional capabilities.
  • Develop reusable patterns and services that accelerate experimentation and reduce time to production.
  • Ensure support for conventional AI-enabled experiences and increasingly autonomous, multi-step agentic workflows.
  • Build a secure, reliable, and scalable platform meeting enterprise-customer and mission-critical workflow requirements.
  • Establish standards for model evaluation, testing, observability, performance, and continuous improvement.
  • Develop approaches to managing AI quality, latency, availability, and cost, including model selection and routing.
  • Partner with Security, Legal, Privacy, and Compliance teams to establish controls for data access, model usage, agent permissions, and execution.
  • Ensure AI systems operate within authorization boundaries with safeguards, traceability, and human oversight for consequential actions.
  • Monitor production performance, identify failures, evaluate outcomes, and improve system behavior over time.
  • Translate platform investments into measurable business and customer outcomes, including faster product development, improved customer experiences, increased automation, and more efficient operations.
  • Establish measures of platform adoption, developer productivity, reliability, AI quality, cost efficiency, and time to production.
  • Prioritize platform investments based on business impact and the capabilities they unlock for multiple product teams.
  • Evolve the platform in response to AI advances, emerging customer needs, and lessons from production deployments.
  • Communicate architectural decisions, investment priorities, trade-offs, and progress to executive leadership.
  • Partner with Product and Engineering leadership to identify shared AI platform capabilities that unlock customer value and accelerate product innovation.
  • Establish platform interfaces, development patterns, documentation, and tools that make AI capabilities accessible to product engineers.
  • Reduce duplicated infrastructure, fragmented implementations, and unnecessary complexity across teams.
  • Define the operating model for collaboration among platform engineering, AI research and application development, product management, and data teams.
  • Foster rapid experimentation, disciplined engineering, and learning from production outcomes.
  • Build a high-caliber AI platform organization combining technical depth, engineering judgment, and an outcome-oriented mindset.
  • Attract and retain talent across AI infrastructure, platform engineering, distributed systems, and applied AI, as appropriate to the mandate.
  • Establish a culture of ownership, technical excellence, accountability, and pragmatic execution while enabling teams to move quickly and maintain enterprise reliability, security, and discipline.
  • Develop strong partnerships across the company and influence through technical credibility and clear articulation of platform value.

About the Company

Zuora

SaaS platform for subscription lifecycle management

Zuora provides a software-as-a-service platform that helps businesses run subscription-based models. It covers the entire customer lifecycle from acquiring customers to billing, payments, and revenue reporting. The core products include Zuora Billing and Zuora RevPro, which automate and streamline order-to-cash processes, recurring revenue management, and revenue recognition. Unlike many generic ERP tools, Zuora combines billing, revenue recognition, and financial reporting for subscription businesses in one system, serving a wide range of customers from startups to Fortune 100 firms and across technology, media, and telecom. Its goal is to enable companies to adopt or optimize subscriptions, scale efficiently, and improve financial operations through automation and integrated analytics.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Redwood City, CA
Founded
2007

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