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About the Opportunity
The Senior Director of AI in Teaching and Learning provides strategic and operational leadership for the integration of artificial intelligence into teaching, learning, and student success across the UNC System. Reporting to the Executive Director of AI Enablement and Impact, this role is responsible for advancing innovative, responsible, and scalable uses of AI in learning environments.
This position leads system\-wide efforts to engage and support instructors, instructional designers and related roles, as well as institutions in navigating and implementing AI\-enabled pedagogical practices, enhancing student learning outcomes, and supporting ethical use of AI in education. The Senior Director develops frameworks, programs, communications and partnerships that translate AI capabilities into meaningful instructional impact, aligning with system priorities and institutional missions. The Senior Director is responsible for identifying, connecting, documenting and elevating faculty led and campus\-based AI projects, helping to translate emerging practices into shared resources, frameworks and scalable models.
The role ensures that successful and evidence\-based AI\-enabled teaching and learning practices are actively identified, amplified, and shared across institutions, building a strong community and accelerating impact.
This position is a hybrid work arrangement and will work at least three days per week onsite at the Dillon building in downtown Raleigh's Warehouse District. UNC System employees are generally required to reside in North Carolina, within a 2\-hour commuting distance of their assigned duty station.
About the UNC System Office
The UNC System Office includes the offices of the President and other senior administrators of the multi\-campus University of North Carolina System. The UNC System is a treasured public institution dedicated to serving the people of North Carolina through world\-class teaching, research, and community engagement. Today, nearly 250,000 students are enrolled in our 16 universities across the state and at the NC School of Science and Mathematics. System Office staff is responsible for executing the policies of the UNC Board of Governors and providing University\-wide leadership in the areas of academic affairs, business and financial management, long\-range planning, student affairs, research, legal affairs, and government relations. The UNC System Office also has administrative oversight of a number of University affiliates, including PBS North Carolina, the North Carolina Arboretum, the NC State Education Assistance Authority, and University of North Carolina Press.
About the Team
The Office of the Chief Operating Officer (COO), reporting directly to the President of the University, is responsible for overseeing internal and campus\-focused operational priorities, including leadership and coordination of key teams such as Human Resources, IT, Enterprise Data, Project Management, Advancement Shared Services, HR Shared Services, Workforce Policy, and Operational Strategy. These divisions support both the UNC system office's internal operations and provide strategic leadership across campus. The COO's office advises the President, fosters relationships on his behalf, and engages with Chancellors to further the President's strategic priorities, ensuring alignment between operational practices and institutional goals while driving efficiency and innovation throughout the University.
Minimum Education, Experience, and Skills
Master’s degree in Education, Instructional Design, Learning Sciences, Educational Technology, or related field with a minimum of 7–10 years of experience in higher education, instructional innovation, or academic technology
Required Competencies
- Demonstrated experience leading teaching and learning initiatives at scale
- Deep understanding of pedagogy, instructional design, and student learning outcomes
- Demonstrated knowledge of AI applications in education, including generative AI
- Strong stakeholder engagement and relationship\-building skills across academic environments
- Experience leading faculty development and adoption of new technologies
- Ability to translate complex technical concepts into practical instructional strategies
- Demonstrated commitment to ethical, equitable, and responsible use of AI
- Strong written and verbal communication skills, including executive\-level communication
- Demonstrated ability to lead change management and drive adoption of new technologies
Preferred Education, Experience, and Skills
- Doctorate in a relevant field
- Experience managing cross\-institutional or system\-wide programs
- Experience working across a higher education system or multi\-institution environment
- Familiarity with AI governance, academic integrity considerations, and policy development
- Experience evaluating teaching innovations and measuring learning impact
- Experience with digital learning platforms, LMS systems, and edtech ecosystems
Classification: IT Instruc/Class Supp Prof IIIAppointment Type: PermanentFull\-Time/Part\-Time: Full\-TimeEmployment Type: EHRAEHRA Category: Exempt Professional StaffLocation: Raleigh, NC USAPosition Number: 20077673Special Instructions to Applicants: The posting remains open until filled, but applications received by Friday, August 7, 2026, will receive priority consideration.
Equal Employment Opportunity and Other Information
The UNC System Office (includes PBS NC, NCSEAA, and NC Arboretum) is an equal opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to age, color, disability, gender identity, genetic information, national origin, race, religion, sex, sexual orientation, or status as a protected veteran.
The UNC System Office (includes PBS NC, NCSEAA, and NC Arboretum) is a VEVRAA Federal Contractor.
To claim veteran's preference, all eligible persons must indicate their eligibility as requested on the application. A DD Form 214, Certificate of Release or Discharge from Active Duty, may be required later in the selection process.
The UNC System Office (includes PBS NC, NCSEAA, and NC Arboretum) participates in E\-Verify. Federal law requires all employers to verify the identity and employment eligibility of all persons hired to work in the United States.
Human Resources Contact Information
Applicants needing assistance with the application process are asked to contact: [email protected].
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 University of North Carolina, 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. Director-level AI roles across all categories have a median of $274,554.
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.
University of North Carolina AI Hiring
University of North Carolina has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US.
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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