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About This Role
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
Required Skills & Qualifications
Product Management
- Proven ability to write crisp product requirements, functional design docs, and user stories with clear acceptance criteria — engineers can build directly from your specs
- Experience running agile ceremonies: sprint planning, backlog grooming, retrospectives, and stakeholder demos
- Skilled at prioritization under ambiguity — you can say no, explain the trade-off, and move the team forward
- Ability to decompose complex workflows into phased delivery plans; familiar with MVP scoping and iterative rollout
- Strong stakeholder management — comfortable facilitating reviews with both senior sponsors and individual contributors
Technical Fluency
- Able to read and reason about API contracts, data schemas, and system diagrams — you don't write production code, but you can review it conceptually
- Working knowledge of AI/ML product capabilities and limitations: RAG, LLM-based classification, agentic workflows — enough to scope feasibly and challenge estimates
- Comfortable 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 support
Domain — 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 analytics
- Familiarity 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 advantage
- Knowledge of compliance and auditability requirements common in enterprise procurement
Communication & Collaboration
- Excellent written communication — your PRDs, status updates, and decision logs are things people actually read
- Experience partnering with data, finance, and legal as internal customers or reviewers
- Comfortable presenting to both technical teams and non-technical business stakeholders, adjusting depth accordingly
Nice to Have
- Experience as a PM or analyst at a CPG, retail, foodservice, or global supply chain organization
- Prior 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 decisions
Basic Qualifications
- Bachelor's degree in Business, Supply Chain, Computer Science, Engineering, or related field
- 3–6 years of product management or product analyst experience, with at least 1–2 years in a technically complex or AI-adjacent product environment
As a Starbucks partner, you (and your family) will have access to medical, dental, vision, basic and supplemental life insurance, and other voluntary insurance benefits. Partners have access to short-term and long-term disability, paid parental leave, family expansion reimbursement, paid vacation from date of hire*, sick time (accrued at 1 hour for every 25 hours worked), eight paid holidays, and two personal days per year. Starbucks also offers eligible partners participation in a 401(k) retirement plan with employer match, a discounted company stock program (S.I.P.), Starbucks equity program (Bean Stock), incentivized emergency savings, and financial well-being tools. Additionally, Starbucks offers 100% upfront tuition coverage for a first-time bachelor’s degree through Arizona State University’s online program via the Starbucks College Achievement Plan, student loan management resources, and access to other educational opportunities. You will also have access to backup care and DACA reimbursement. Starbucks will comply with any applicable state and local laws regarding employee leave benefits, including, but not limited to providing time off pursuant to the Colorado Healthy Families and Workplaces Act, and in accordance with its plans and policies. This list is subject to change depending on collective bargaining in locations where partners have a certified bargaining representative. For additional information regarding partner perks and more detailed information about benefits, go to starbucksbenefits.com.
- If you are working in CA, CO, IL, LA, ME, MA, NE, ND or RI, you will accrue vacation up to a maximum of 120 hours (190 in CA) for roles below director and 200 hours (316 in CA) for roles at director or above. For roles in other states, you will be granted vacation time starting at 120 hours annually for roles below director and 200 hours annually for roles director and above.
The actual base pay offered to the successful candidate will be based on multiple factors, including but not limited to job-related knowledge/skills, experience, geographical location, and internal equity. At Starbucks, it is not typical for an individual to be hired at the high end of the range for their role, and compensation decisions are dependent upon the facts and circumstances of each position and candidate.
We believe we do our best work when we're together, which is why we're onsite four days a week.
Join us and inspire withevery cup. Apply today!
Starbucks Coffee Company is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, or protected veteran status, or any other characteristic protected by law.
Qualified applicants with criminal histories will be considered for employment in a manner consistent with all federal, state and local ordinances. Starbucks Coffee Company is committed to offering reasonable accommodations to job applicants with disabilities. If you need assistance or an accommodation due to a disability, please contact us at [email protected] or 1(888) 611-2258.
Salary Context
This $108K-$181K range is in the lower quartile for AI Product Manager roles in our dataset (median: $185K across 149 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 10,827 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Starbucks, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,350 based on 994 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 34% below the category median. Disclosed range: $108K to $181K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Starbucks AI Hiring
Starbucks has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Nashville, TN, US, Seattle, WA, US. Compensation range: $181K - $319K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (122) are outnumbered by mid-level (5,256) and senior (3,922) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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