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About This Role
It's fun to work in a company where people truly believe in what they are doing. At Dutch Bros Coffee, we are more than just a coffee company. We are a fun\-loving, mind\-blowing company that makes a difference one cup at a time.
Position Overview:
At Dutch Bros, we believe great technology should be simple, intuitive, and enable our crews to focus on what matters most—creating amazing experiences for our customers. As an Employee Experience Engineer, you'll help shape how employees interact with technology by simplifying processes, improving self\-service, and designing intelligent experiences powered by AI and automation. This role sits at the intersection of technology, operations, and employee experience. You'll analyze how employees work, identify friction points, and partner across the business to build scalable solutions that reduce effort, improve productivity, and continuously elevate the technology experience. Whether it's streamlining onboarding, improving software requests, building AI\-powered workflows, maturing our knowledge base, or creating an intuitive service catalog, you'll play a key role in transforming how Technology Operations supports the business.
Job Qualifications:
- Bachelor’s degree in a relevant field (business, finance, economics, computer science, data science, etc.) preferred.
- 3\+ years of experience
- Experience with IT Service Management platforms such as Zendesk, ServiceNow, Freshservice, Jira Service Management, or similar.
- Experience building workflows, automation, integrations, or low\-code solutions.
- Experience managing or improving knowledge bases and service catalogs.
- Familiarity with AI technologies including copilots, virtual agents, prompt engineering, or intelligent automation.
- Strong analytical, problem\-solving, and process improvement skills.
- Excellent communication and collaboration skills with both technical and business teams.
- Passion for creating exceptional employee experiences through technology.
Location Requirement:
This role is located in Tempe, Arizona. This position is required to be in office 4 days per week (Mon\-Thurs); Fridays are optional remote work days.
Key Result Areas (KRAs):
Employee Experience
- Design technology experiences that are simple, intuitive, and employee\-focused.
- Analyze employee journeys to identify opportunities to eliminate friction.
- Partner with stakeholders to continuously improve how employees access technology and services.
- Champion a culture of continuous improvement and service excellence.
AI \& Intelligent Automation
- Identify opportunities to automate repetitive work using AI and workflow automation.
- Design and implement intelligent workflows that improve speed, consistency, and employee satisfaction.
- Support AI\-powered assistants, self\-service capabilities, and digital experiences.
- Evaluate emerging AI technologies and recommend practical business use cases.
Knowledge \& Service Catalog
- Own the strategy, governance, and continuous improvement of the Technology Operations knowledge base.
- Ensure knowledge is accurate, searchable, and optimized for both employees and AI.
- Design and mature a modern service catalog that simplifies requesting technology services.
- Partner with service owners to create clear, standardized service offerings.
Process Engineering \& Service Design
- Review existing operational processes and redesign them for scalability.
- Reduce manual work through automation and intelligent workflow design.
- Standardize employee lifecycle processes across Technology Operations.
- Help create repeatable, measurable service experiences.
Analytics \& Continuous Improvement
- Measure adoption of AI, automation, self\-service, knowledge, and catalog experiences.
- Analyze support trends and employee feedback to prioritize improvements.
- Define success metrics and demonstrate business value from automation initiatives.
- Continuously identify opportunities to improve efficiency and employee satisfaction.
Skills:
- Employee Experience
- Self\-Service Adoption
- AI \& Automation Utilization
- Knowledge Quality \& Adoption
- Service Catalog Maturity
- Reduction in Manual Processes
- Employee Effort Score
- Operational Efficiency
- Service Quality \& Consistency
Physical Requirements:
- In\-Office Environment:Must be able to work in a busy, crowded, and loud office with frequent distractions and interruptions
- Must be able to collaborate in\-person with occasional impromptu in\-person meetings
- Office Conditions:Adaptability to typical office conditions, which may include exposure to air conditioning, heating, artificial lighting, and varying noise levels
- Mobility: Ability to sit, stand, reach, twist, stretch, and work at a desk for long stretches. Must be able to occasionally move or lift office items up to 25 pounds
- Hearing Requirements:Hearing must be sufficient or correctable to ensure clear understanding of spoken information, including participating in virtual meetings and phone calls. Use of hearing aids or other assistive devices is acceptable if needed.
- Reading and Writing Proficiency:Ability to read and write in English is essential for processing documents, drafting reports, and following up on necessary actions. Proficiency in written communication is required to handle job\-related tasks effectively.
- Vision Requirements:Vision must be adequate or correctable to perform essential job duties, such as reading documents on a computer screen and using other visual tools. Use of corrective lenses or other measures to meet visual requirements is expected if needed.
- Technology Proficiency:Must be proficient in operating a computer and other office productivity tools such as printers, scanners, and collaboration software.
- Effective Communication:Must possess strong verbal and written communication skills to interact effectively with team members, clients, and other stakeholders via email, video conferencing, and other in office communication tools.
Compensation:
DOE
If you like wild growth and working in a unique and fun environment, surrounded by positive community, you'll enjoy your career with us!
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Dutch Bros Coffee, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Dutch Bros Coffee AI Hiring
Dutch Bros Coffee has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tempe, AZ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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