technical product manager - AI

Technical Product ManagerFrom 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 ResponsibilitiesFunctional Design & Product DocumentationOwn 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 requirementsDefine and prioritize AI-enabled features across the platform, balancing business value, data readiness, and technical feasibilityCoordinate with business stakeholders to resolve open data, integration, and policy questions that would otherwise block engineeringDrive each module's functional spec from draft through stakeholder sign-off without losing momentumWrite clear, engineer-ready user stories and acceptance criteria across all modules; keep the sprint backlog current and prioritizedStakeholder & Gate ManagementFacilitate 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 recordDrive AI governance checkpoints in partnership with Security, Architecture, and Legal — covering AI ethics, data privacy, model validation, and production readinessPartner with procurement, supply chain, finance, and legal stakeholders to gather requirements, resolve open questions, and get decisions without engineering being blockedRepresent the team in leadership demos and status reviews; translate engineering delivery progress into a clear business-level narrativeDelivery & RoadmapOwn 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 priorityRun agile ceremonies (sprint planning, backlog grooming, demos, retrospectives); manage the cross-module sprint backlog alongside the Principal EngineerParticipate in UAT and AI output reviews — you hold the bar on whether a release is ready for stakeholdersRequired Skills & QualificationsProduct ManagementProven ability to write crisp product requirements, functional design docs, and user stories with clear acceptance criteria — engineers can build directly from your specsExperience running agile ceremonies: sprint planning, backlog grooming, retrospectives, and stakeholder demosSkilled at prioritization under ambiguity — you can say no, explain the trade-off, and move the team forwardAbility to decompose complex workflows into phased delivery plans; familiar with MVP scoping and iterative rolloutStrong stakeholder management — comfortable facilitating reviews with both senior sponsors and individual contributorsTechnical FluencyAble to read and reason about API contracts, data schemas, and system diagrams — you don't write production code, but you can review it conceptuallyWorking knowledge of AI/ML product capabilities and limitations: RAG, LLM-based classification, agentic workflows — enough to scope feasibly and challenge estimatesComfortable working in Jira, Linear, or equivalent for backlog management; familiar with Git-based doc workflows (Markdown, PR-based review)Can interpret dashboards and basic data queries to investigate product questions without always needing engineering supportDomain — Sourcing & Procurement *(Strongly Preferred)*Hands-on experience in or building products for Source-to-Contract (S2C) or Procure-to-Pay (P2P) workflows — e.g., RFx, supplier evaluation, contract lifecycle, commodity management, or spend analyticsFamiliarity with enterprise procurement platforms (Coupa, SAP Ariba, Oracle, Jaggaer, or similar)Understanding of sourcing calendars, commodity price benchmarking, supplier performance management, or contract analytics is a meaningful advantageKnowledge of compliance and auditability requirements common in enterprise procurementCommunication & CollaborationExcellent written communication — your PRDs, status updates, and decision logs are things people actually readExperience partnering with data, finance, and legal as internal customers or reviewersComfortable presenting to both technical teams and non-technical business stakeholders, adjusting depth accordinglyNice to HaveExperience as a PM or analyst at a CPG, retail, foodservice, or global supply chain organizationPrior work on AI-enabled enterprise products (not just traditional SaaS)Familiarity with engineering delivery metrics (e.g. deployment frequency, lead time, incident rate) and how they inform product and roadmap decisionsBasic QualificationsBachelor's degree in Business, Supply Chain, Computer Science, Engineering, or related field3–6 years of product management or product analyst experience, with at least 1–2 years in a technically complex or AI-adjacent product environment