technical product manager - AI

From the beginning, Starbucks set out to be a different kind of company—one that not only celebrated coffee and its rich tradition, but also built meaningful connections. We are known for developing extraordinary leaders who are guided by service to others and driven to make an impact at global scale.

We are seeking a Technical Product Manager to bridge business stakeholders and our AI engineering team — translating sourcing and procurement workflows into clear, actionable product specs that an AI team can build against. You are equally comfortable in a stakeholder review, a data schema discussion, and a sprint retro. You bring domain depth in sourcing and procurement, and enough technical fluency to be a credible partner to engineers working on LLM-powered systems.

Summary of Key Responsibilities

Functional Design & Product Documentation

• Own the functional design specifications and product backlog across the AI-enabled sourcing platform — you are the author and accountable party for specs that engineering builds from directly\:
• Translate sourcing and procurement workflows (calendaring, market intelligence, contract management, commodity analysis) into clear, engineer-ready product requirements
• Define and prioritize AI-enabled features across the platform, balancing business value, data readiness, and technical feasibility
• Coordinate with business stakeholders to resolve open data, integration, and policy questions that would otherwise block engineering
• Drive each module's functional spec from draft through stakeholder sign-off without losing momentum
• Write clear, engineer-ready user stories and acceptance criteria across all modules; keep the sprint backlog current and prioritized

Stakeholder & Gate Management

• Facilitate the structured sign-off gates that govern each module's progression from product requirements through functional design, technical design, QA, and release — no phase advances without the right approver on record
• Drive AI governance checkpoints in partnership with Security, Architecture, and Legal — covering AI ethics, data privacy, model validation, and production readiness
• Partner with procurement, supply chain, finance, and legal stakeholders to gather requirements, resolve open questions, and get decisions without engineering being blocked
• Represent the team in leadership demos and status reviews; translate engineering delivery progress into a clear business-level narrative

Delivery & Roadmap

• Own the phased delivery plan across releases\: track which features are in the current MVP, which are deferred to the next release (data integration connectors, access control, document export), and which require AI enabler work before they can be built (LLM-powered features)

• Coordinate data readiness with business partners — live enterprise system integration is the team's top cross-module blocker; coordinating

  sample data, field mappings, and system-of-record decisions is an immediate priority

• Run agile ceremonies (sprint planning, backlog grooming, demos, retrospectives); manage the cross-module sprint backlog alongside the Principal Engineer
• Participate in UAT and AI output reviews — you hold the bar on whether a release is ready for stakeholders