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About This Role
This position contributes to Starbucks success by leading and directing a team of procurement professionals to deliver best total value global sourcing strategies. Develops a vision for best\-in\-class sourcing for the spend category. Establishes multi\-year plans to enable business goals. Champions corporate social responsibility and supplier diversity leadership programs. Collaborates internally at all levels, across the US and international business units, and across the supply base to support business sourcing objectives.
Models and acts in accordance with Starbucks guiding principles.
Summary of Key Responsibilities
Responsibilities and essential job functions include but are not limited to the following: Leadership \- Setting goals for the work group, developing organizational capability, and modeling how we work together:
- Identifies and communicates key responsibilities and practices to ensure the immediate team of direct reports promotes a successful attitude, confidence in leadership, and teamwork to achieve business results
- Supports the implementation of company programs to ensure the success of the Company.
- Successfully collaborate and motivate across the organization
- Rapidly assess opportunities, develop a compelling vision that will enable best in class performance, and lead teams to implement systematic change
- Build and lead global procurement teams
- Lead teams to higher levels of performance
- Develop people and teams
- Elevate organizational capability across procurement organization
- Successfully influence internal stakeholders through the executive vice president level
Planning and Execution \- Developing strategic and operational plans for the work group, managing execution, and measuring results:
- Prepares, communicates and educates client groups and team on changes in policies and practices within the organization
- Plans and manages business unit and department processes and practices to ensure that programs are aligned with company business goals and objectives.
Business Requirements \- Providing functional expertise and executing functional responsibilities:
- Develop compelling vision to enables best in class sourcing for the relevant spend area and the corporation overall
- Establish multi\-year strategic plans and goals to enable bottom\-line and top\-line company growth
- Champion corporate social responsibility and supplier diversity leadership programs
- Design and develop organization with superior engagement to consistently achieve goals
- Collaborate across internal teams, across the supply base, and across the appropriate industry
- Develop and execute sourcing strategies and ratify commodity risk management recommendations
- Drive major step change within scope of responsibility
- Manage the sourcing strategies in areas of high financial or brand risk to Starbucks
- Operate in high\-growth climate
Supplier Engagement and Management:
- Develop and leverage strong professional relationships with strategic suppliers and industry players
- Employ market leading knowledge of key global industry players, competitors and dynamics to identify potential
- Proficiently manage suppliers for continuous performance improvement
- Successfully influence external company presidents, as well as governmental policy or standard practice within the industry
Cross\-Functional Projects:
- Effectively utilize program and project management tools and techniques
- Effectively manage multiple competing projects and deadlines
- Efficiently solve problems and make decisions for primary and strategic, unique or complex issues across a vast array of projects and processes
- Provide broad oversight and drive execution of project plans
Partner Development \& Team Building \- Providing partners with coaching, feedback, and developmental opportunities and building effective teams:
- Challenges and inspires team members to achieve business results.
- Conducts and ensures the completion of performance reviews.
- Ensures partners adhere to legal and operational compliance requirements.
- Oversees training and development of partners directly and indirectly managed and makes effective staffing decisions.
- Provides coaching, direction and leadership support to team members in order to achieve partners, business and customer results
- Summary of Experience Years
- Team leadership and people management (10 yrs)
- Global strategic sourcing/ procurement experience, preferably in a high growth, retail/manufacturing/CPG environment (12 yrs)
- Ability to apply knowledge of multidisciplinary business principles and practices to achieve successful outcomes in cross\-functional projects and activities
- Analytical: Advanced analytical skills, adept in business planning, financial management, and budget management
- Procurement: Market leading knowledge of global market analysis, macro\-economic drivers, sourcing category strategy, supplier relationship management, strategic vendor alliance programs, ‘should\-cost’ models, and multi\-year negotiation strategies, supplier continuous improvement programs/Lean Initiatives Legal: Knowledge of critical procurement legal requirements and contracting best practices
- Ethics: Knowledge of business ethics and Starbucks Ethical Sourcing Requirements
- Influence: Exceptional ability to influence at a Senior Executive Level both internally and externally. Able to influence relevant industry and governmental policy issues
- Communication: Exceptional communication both written and oral\- Systems: Knowledge of Excel, Oracle, BI Apps, charting programs, PowerPoint, and others
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.
*We are hiring for direct and indirect sourcing teams, that will support our North America operations. This reflects our long\-term commitment to innovation, opportunity, and shared success.*
*These roles will be based in Nashville, Tennessee, where we plan to expand our presence with the opening of a new Starbucks office later this year.*
*We look forward to establishing strong roots in this growing city and contributing meaningfully to the local community.*
Join us and inspire with every 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* *applicantaccommodation@starbucks.com* *or 1(888\) 611\-2258\.*
Salary Context
This $146K-$244K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Starbucks, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($195K) sits 17% above the category median. Disclosed range: $146K to $244K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Starbucks AI Hiring
Starbucks has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Seattle, WA, US, New York, NY, US, Nashville, TN, US. Compensation range: $243K - $244K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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