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Now Brewing – Engineer (Principal) AI Experiences, Green Dot Assist! #tobeapartner
From the beginning, Starbucks set out to be a different kind of company. One that not only celebrated coffee and the rich tradition, but that also brought a feeling of connection. We are known for developing extraordinary leaders who share this passion and are guided by their service to others.
As a Principal Engineer (AI) , you’ll play a critical role in delivering high-quality, reliable, and secure Generative AI technology that enables coffee house partners and leaders to operate effectively, deliver results with quality and be more customer focused. This advanced role focuses on owning complex engineering capabilities end-to-end—partnering across business and technology teams to bring in AI capabilities where it matters most in a cost effective, performant way with reliability, scalability, and operational excellence at the core.
This role supports the development of artificial intelligence, multi-agent orchestration strategies, application architecture, seamless integrations with user experience, and building right security, legal and governance controls with Responsible AI principles. The successful candidate will be responsible for shaping how a multi-agent orchestration engine and deliver unified experiences to coffee house partners and leaders to effectively operate and bring needed customer focus to coffeehouse operations.
The ideal candidate will support the development of the architecture, standards, and AI engineering best practices that help scale and mature a multi-agent eco-system in a governed , scalable, and secure user experiences. This includes defining how conversational interfaces, intelligent applications, workflow systems, analytical tools, and decision-support technologies come together as a cohesive ecosystem.
A key aspect of the role is helping the organization evolve from fragmented reporting and application experiences toward unified, AI-enabled platforms that deliver context-aware insights, recommendations, automation, and action-oriented decision support.
As a principal engineer, AI Experiences, Green Dot Assist, you will...
Define the Future of Enterprise AI Experiences
- Establish the long-term vision for how users discover, access, and interact with enterprise knowledge, analytics, and intelligent systems.
- Define architectural patterns for conversational interfaces, AI assistants, recommendation engines, and agent-based solutions.
- Create a cohesive strategy that connects data, analytics, workflows, and AI into seamless business experiences.
- Guide the evolution toward proactive, context-aware decision support and automation.
Lead Enterprise Application Platform Strategy
- Define the architecture of modern business applications built on governed enterprise data.
- Establish standards for application hosting, user experience, integration, deployment, and lifecycle management.
- Create scalable frameworks that allow teams to rapidly develop applications while maintaining consistency, governance, and security.
- Drive simplification and rationalization across overlapping application and reporting technologies.
Create a Unified User Experience Architecture
- Support leaders to establish principles that provide users with a consistent way to discover and access business capabilities.
- Enable a marketplace-style experience that balances self-service innovation with governance and operational control.
- Ensure experiences remain intuitive regardless of the underlying technology stack.
Drive AI and Intelligent Automation Adoption
- Partner with business and technology teams to identify opportunities where AI can accelerate decision making and reduce manual effort.
- Define reusable patterns for intelligent workflows and automated actions.
- Establish standards for integrating AI capabilities into operational and analytical experiences.
- Promote responsible and governed adoption of emerging AI technologies.
Advance Context & Knowledge Engineering
- Support and champion the development of semantic models, metadata strategies, business ontologies, and knowledge frameworks that improve AI effectiveness.
- Enable systems to understand relationships between data, processes, users, and business concepts.
- Drive contextualization strategies that improve discoverability, reuse, and decision quality.
- Establish foundations for enterprise-scale intelligent systems.
Governance, Security & Platform Standards
- Support leaders in the development of scalable access control and authorization patterns for business-facing applications and AI experiences.
- Create reference architectures and engineering standards that support secure, compliant, and maintainable systems.
- Ensure innovation can occur without creating fragmentation, duplication, or operational risk.
Technical Leadership
- Serve as a recognized technical authority across multiple organizations.
- Influence architecture strategy at senior leadership levels.
- Mentor engineers, architects, and technical leaders.
- Lead architecture reviews, technology evaluations, and standards development.
- Drive alignment across engineering, product, security, user experience, and business stakeholders.
We’d love to hear from people with...
Required Qualifications
- 10+ years of experience in software engineering, data engineering, platform engineering, enterprise architecture, or related disciplines.
- Demonstrated experience designing and scaling enterprise platforms and distributed systems.
- Deep understanding of cloud-native architectures, APIs, application frameworks, and modern software delivery practices.
- Experience designing systems that balance usability, governance, security, and scalability.
- Strong background in analytics, AI, data-powered applications, or intelligent systems.
- Proven ability to influence technical direction across organizational boundaries.
- Exceptional communication and stakeholder management skills.
- Experience with generative AI, conversational interfaces, intelligent agents, or recommendation systems.
Preferred Qualifications
- Knowledge of metadata management, semantic technologies, knowledge graphs, and ontology-driven architectures.
- Experience designing highly governed enterprise applications and platforms.
- Familiarity with enterprise-scale analytics and decision-support environments.
- Experience integrating operational workflows with analytical and AI-driven capabilities.
What Success Looks Like:
Within the first 12–18 months, this leader will:
- Define a clear enterprise strategy for Multi-Agent AI-enabled business experiences.
- Establish architectural patterns for intelligent applications and decision-support systems.
- Create a scalable framework for application development and governance.
- Reduce fragmentation across enterprise applications, reporting tools, and analytical experiences.
- Enable teams to innovate rapidly while maintaining consistency, security, and operational excellence.
- Improve access to trusted information and accelerate decision making across the organization.
- Lay the foundation for a future where intelligent systems proactively assist users, recommend actions, and automate routine decision-making processes.
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 $177K-$295K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($236K) sits 12% above the category median. Disclosed range: $177K to $295K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Starbucks AI Hiring
Starbucks has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Seattle, WA, US, Nashville, TN, US. Compensation range: $194K - $295K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
Frequently Asked Questions
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