Interested in this AI/ML Engineer role at Capella University?
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This position serves as a business expert and is responsible for the end\-to\-end ideation, design, launch, optimization and support of emerging, mission\-critical products focused on AI, automation, and intelligent operations. This role will lead cross\-functional efforts to identify, shape, and scale new capabilities that transform core business processes and student\-facing experiences. This role balances high\-visibility business engagement with a practical understanding of how AI and automation capabilities are applied within real\-world processes. The Product Owner must be fluent in translating business problems into AI\-enabled requirements, prototypes, and other solutions, and working closely with technical partners to deliver value at scale.
The Product Owner will operate in a highly dynamic environment where product scope is not fully predefined, requiring strong problem framing, opportunity identification, and iterative product development. This individual will partner across the enterprise to bring to life solutions such as virtual agents, AI\-enabled processes, and intelligent workflow automation that improve outcomes across enrollment, operations, and student experience while managing the roadmap, cross\-functional leadership, and end\-to\-end product accountability for these solutions.Essential Duties \& Responsibilities:
- Own end\-to\-end product lifecycle for a portfolio of emerging AI and automation products, from concept through launch, continuous improvement and issues triage and resolution.
- Identify and define new product opportunities by assessing business pain points, operational inefficiencies, and student experience gaps.
- Develop, own, and continuously refine product roadmaps in an environment where product scope and priorities evolve over time.
- Translate business needs into epics, features, and user stories; manage and prioritize backlog.
- Partner with technical teams to shape AI/automation solutions while remaining accountable for business outcomes.
- Lead cross\-functional collaboration across Enrollment, Advising, Operations, Marketing, Analytics, and IT to design, run and optimize integrated solutions.
- Drive adoption and value realization of AI\-enabled capabilities such as:
+ Virtual agents (e.g., lead qualification, chatbot experiences)
+ Intelligent workflow automation
+ AI\-driven knowledge management
- Ensure solutions are grounded in measurable outcomes (e.g., conversion, productivity, experience, persistence).
- Analyze data and performance metrics to evaluate product success and inform iteration.
- Design and execute experiments (A/B tests, pilots) to validate product hypotheses and refine solutions.
- Stay current on emerging AI and automation trends, tools, and best practices, translating them into practical business applications.
- Support broader strategic initiatives by introducing innovative, scalable solutions that improve both learner and operational outcomes.
Job Skills:
- Strong ability to translate business problems into structured product opportunities and roadmaps.
- Demonstrated understanding of AI and automation use cases in business processes (e.g., conversational AI, workflow automation, decision support).
- Ability to bridge business and technical teams without requiring deep engineering expertise.
- Experience working in Agile environments, including backlog management, story development, prioritization and sprint planning.
- Exceptional communication skills with the ability to influence across all levels of the organization.
- Strong analytical mindset with the ability to interpret data and drive decision\-making.
- Customer\-first mindset; experience with human\-centered design, service design or Lean transformation is preferred.
- Comfort operating in ambiguity and building products where requirements are not fully defined upfront.
- High degree of ownership, accountability, and initiative.
- Ability to learn and apply new technologies quickly, particularly in the AI/automation space.
Work Experience:
- Minimum of 6 years of professional experience.
- Minimum of 4\-6 years in product ownership, product management, or equivalent experience leading cross\-functional initiatives.
- Demonstrated experience driving initiatives involving AI, automation, or advanced analytics in business operations.
+ This may include implementing AI\-enabled tools, automation of workflows, or intelligent customer/learner experiences.
- Experience does not need to be in IT or software development but must include meaningful involvement in delivering AI/automation solutions (beyond basic tool adoption).
Education:
- Bachelor’s degree required from a regionally accredited institution.
- Preferred (but not required):
+ Coursework, certification, or formal training in AI, data, analytics, or automation
+ OR equivalent practical experience applying these capabilities in business contexts
Other:
- Must be able to travel occasionally should a business need arise. For most roles travel would not be common. Travel may involve plane, car or metro. In accordance with ADA policies, reasonable accommodation regarding travel limitations can be provided. Travel will be more common for roles such as Account Executives (25 \- 50%), senior leaders (10 – 20%) or Capella Core Faculty (5 – 10%).
- Ability to work onsite in Corporate or Campus location (in a typical office environment) may be required based on role. If so, this would include being mobile within the office, including movement from floor\-to\-floor using elevators or stairs.
- If offsite or hybrid role, must have access to work in settings which enables meeting all requirements of the role (including privacy, reliable internet access, phone, ability to video conference, etc.) at a remote location.
- Faculty and Federal Work Study roles require access to work in setting which enables meeting all requirements of the role (including computer, privacy, reliable internet access, phone, ability to video conference, etc.) at a remote location.
- This role may require lifting, however reasonable accommodation will be provided in accordance with our ADA policies.
- Must be able to meet critical thinking and problem solving aspects aligned to job duties, as well as effectively communicating with co\-workers.
- Must be able to work more than 40 hours per week when business needs warrant. Accommodations related to schedule may be considered.
- Able to access information using a computer.
- Other essential functions and marginal job functions are subject to modification.
SEI offers a comprehensive package of benefits to employees scheduled 30 hours or more per week. In addition to medical, dental, vision, life and disability plans, SEI employees may take advantage of well\-being incentives, parental leave, paid time off, certain paid holidays, tax saving accounts (FSA, HSA), 401(k) retirement benefit, Employee Stock Purchase Plan, tuition assistance as well as entertainment and retail discounts. Non\-exempt employees are eligible for overtime pay, if applicable.
Careers \- Our Benefits, Strategic Education, Inc
SEI is an equal opportunity employer committed to fostering an inclusive and collaborative culture where individuals can grow their careers and contribute fully. We strive to attract talent with broad experiences, skills and perspectives. We welcome applications from all. While it is not typical for an individual to be hired at or near the top end of the pay range at SEI, we offer a competitive salary. The actual base pay offered to the successful candidate may vary depending on multiple factors including, but not limited to, job\-related knowledge/skills, experience, business needs, geographical location, and internal pay equity. Our Talent Acquisition Team is ready to discuss your interest in joining SEI. The expected salary range for this position is below.
$106,500\.00 \- $159,800\.00 \- Salary *If you require reasonable accommodations to complete our application process, please contact our Human Resources Department at* *[email protected]*.
Salary Context
This $106K-$159K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Capella University, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($133K) sits 38% below the category median. Disclosed range: $106K to $159K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Capella University AI Hiring
Capella University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $159K - $159K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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