Interested in this AI/ML Engineer role at University of North Carolina at Chapel Hill?
Apply Now →Skills & Technologies
About This Role
Posting Information
Department SOP\-Eshelman Innovation\-450300
Career Area Research Professionals
Posting Open Date 06/24/2026
Application Deadline 07/27/2026
Open Until Filled No
Position Type Permanent Staff (EHRA NF)
Working Title Director of Studio Operations and AI Strategy, Eshelman Innovation
Appointment Type EHRA Non\-Faculty
Position Number 20076943
Vacancy ID NF0009847
Full Time/Part Time Full\-Time Permanent
FTE 1
Hours per week 40
Position Location North Carolina, US
Hiring Range $75,321 to $85,857
Proposed Start Date 08/01/2026
Position Information
Be a Tar Heel!
A global higher education leader in innovative teaching, research and public service, the University of North Carolina at Chapel Hill consistently ranks as one of the nation’s top public universities. Known for its beautiful campus, world\-class medical care, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn.
One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and professional opportunities that span the campus and community.
University employees can choose from a wide range of professional training opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks for numerous retail, restaurant and performing arts discounts, savings on local child care centers and special rates on select campus events. UNC\-Chapel Hill offers full\-time employees a comprehensive benefits package, paid leave, and a variety of health, life and retirement plans and additional programs that support a healthy work/life balance.
Primary Purpose of Organizational Unit
The UNC Eshelman School of Pharmacy (www.pharmacy.unc.edu) is one of six health science schools (Pharmacy, Nursing, Dentistry, Social Work, Public Health, Medicine) at the University of North Carolina at Chapel Hill and is one of the oldest health science academic programs at Chapel Hill. The School teaches approximately 600 PharmD students and 145 graduate students. The School has built a reputation for its continued pursuit of excellence, rigorous education and training programs, cutting\-edge multidisciplinary research, progressive pharmacy practices, efficient business operations, and its outstanding faculty, staff, and students. The School was named the number one School of Pharmacy in the U.S. by the U.S. News and World Report. The School has experienced unprecedented growth and success and continues to strategically position itself for sustained impact, as articulated in the School’s Strategic Plan (https://pharmacy.unc.edu/about/oe/strategic\-plan/).
Our Vision is to be the global leader in pharmacy and pharmaceutical sciences. Our Mission is to prepare leaders and innovators to solve the world’s most pressing health care challenges. We are Advancing Medicine for Life through innovation and collaboration in pharmacy practice, education, research, and public service.
Position Summary
The Director of Studio Operations and AI Strategy will play a key role in operationalizing and scaling the Kairos Venture Studio’s systems, tools, and programs across UNC and beyond. Kairos is a university\-based venture studio at UNC Chapel Hill that partners faculty researchers with entrepreneurial student talent to build AI\-native, venture\-backable startups. Each Kairos sprint is a 6\-month intensive program focused on validating a high\-impact problem, positioning a compelling solution, and launching a de\-risked company. Venture teams are composed of a faculty\-PI innovator, student interns and/or fellows that help the faculty member build out the venture. The Venture Studio operates in continuous, recurring sprint cycles (each approximately 6 months), and this position provides year round leadership across the entire lifecycle of these repeating programs. Reporting to the Managing Director, this leader will translate strategic vision into repeatable, AI\-enhanced workflows and infrastructure that support venture creation and team development.
This role is ideal for an experienced innovator who thrives at the intersection of strategy, technology, digital health, and entrepreneurship and who wants to shape how universities and founders build ventures in the GenAI era through scalable systems and operational excellence.
Key Responsibilities:
- Venture Methodology Implementation: Refine and operationalize and implement a repeatable venture Studio framework tailored to healthcare innovation. Develop tools, playbooks, and training materials that guide teams from discovery through launch and support replication across additional UNC Chapel Hill and UNC System School partners.
- Program Execution: Manage and deliver high\-impact programs that transform research or healthcare expertise into scalable startups. Provide year round leadership for continuous sprint cycles and support expansion of the program across multiple schools or institutions. Recruit and coach mission\-driven students, researchers, and operators into strong, cohesive venture teams.
- GenAI Integration: Embed Generative AI into the venture\-building process to enhance validation, market analysis, and business modeling applied as a program scaling mechanism (training/configuring tools; not software development) and create scalable AI workflows and toolkits to accelerate decision\-making.
- Talent Development: Build and manage systems that connects talent with hands\-on innovation opportunities across Eshelman Innovation ventures and programs, ensuring scalable and sustainable talent pipelines for multiple concurrent cohorts.
- Partnerships \& Portfolio: Advise early\-stage teams on strategy and investor readiness. Strengthen collaborations with mentors, investors, and partners across the innovation ecosystem, including cross campus and UNC System School collaborators participating in the venture Studio model.
- Knowledge Management: Develop a digital knowledge base and playbooks to capture and scale the Kairos venture model for use across universities and partner organizations. Ensure consistent documentation and continuous improvement of Studio operations.
Minimum Education and Experience Requirements
Master’s or Bachelor’s and 0\-2 years’ experience; or will accept a combination of related education and experience in substitution.
Required Qualifications, Competencies, and Experience
- AI literacy for program enablement (training/configuring tools, building prompt libraries/knowledge bases; not software development) with working familiarity using platforms such as Microsoft Copilot, OpenAI ChatGPT, Anthropic Claude, AWS services, and Notion AI.
- Experience contributing to or leading programs, teams, or strategic initiatives in innovation, entrepreneurship, or early\-stage venture environments
- Strong communication and interpersonal skills, with the ability to collaborate across diverse teams, including students, faculty, and external partners
- Proven ability to develop or operationalize repeatable systems, workflows, or training programs from the ground up
- Self\-starter comfortable operating in ambiguous, fast\-paced, and distributed environments
- Strong organizational and project management skills, with a track record of executing complex initiatives from ideation to implementation
Preferred Qualifications, Competencies, and Experience
- Advanced degree (MBA, MPH, PhD, or equivalent) in a relevant field such as business, public health, technology, or education
- 2 or more years of professional experience in innovation strategy, venture building, startup acceleration, or digital health
- Prior experience working in a university setting, tech transfer, or academic innovation program
- Familiarity with venture studio or accelerator models
- Experience advising or supporting early\-stage startup teams, especially in healthcare, life sciences, or digital health sectors
- Demonstrated ability to design or scale innovation programs from 0 1
Special Physical/Mental Requirements
Campus Security Authority Responsibilities
Not Applicable.
Salary Context
This $75K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At University of North Carolina at Chapel Hill, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $75K to $85K.
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.
University of North Carolina at Chapel Hill AI Hiring
University of North Carolina at Chapel Hill has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chapel Hill, NC, US. Compensation range: $85K - $110K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.