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About This Role
Posting Information
Posting Number PG194690EP
Internal Recruitment No
Working Title Data Science Librarian
Anticipated Hiring Range $77,000 minimum
Work Schedule University Business hours are Monday \- Friday 8AM \- 5PM. The Libraries supports flexible work arrangements based on departmental and operational needs. This position will require frequent onsite work. Exempt position.
Job Location Raleigh, NC
Department Libraries
About the Department
Join the NC State University Libraries and help us create adventurous, innovative services and library spaces that delight students, faculty, and researchers. The award\-winning James B. Hunt Jr. Library on NC State’s Centennial Campus, offers access to advanced technologies that are enabling revolutionary ways to see and use information. Capturing NC State’s spirit of innovation in education and research, the Hunt Library is recognized as one of the world’s most creative and inspirational learning and collaborative spaces and a model for “the library of the future.” The D. H. Hill Jr. Library, serving the main campus, combines the best of tradition and innovation, housing special collections and a beautiful gallery alongside vibrant, experiential spaces for making, digital media creation, data analysis and visualization, and collaboration. Three branch libraries focus on design, natural resources, and veterinary medicine.
The NC State University Libraries invites applications and nominations for the position of Data Science Librarian. The Data Science Services (DSS) department (6 librarians, 1 library specialist, plus graduate student assistants) helps students and other researchers develop critical data science and visualization skills through expert consultation, workshops, and classroom instruction. The department provides support for a wide range of data lifecycle activities including data collection, discovery, access, evaluation, cleaning, preprocessing, transformation, modeling, analysis, geospatial techniques, and visualization. The department is the service owner for the Dataspace in the Hunt Library and the Data Experience Lab in the Hill Library, community\-oriented spaces offering specialized computing, expert assistance, and services supporting data science and visualization. DSS collaborates with others in the Libraries and with NC State’s Data Science and AI Academy (DSA), a university\-wide effort to meet the university’s growing needs for data science research, education, and expertise.
Wolfpack Perks and Benefits
As a Pack member, you belong here, and can enjoy exclusive perks designed to enhance your personal and professional well\-being. As you consider this opportunity, we encourage you to review our Employee Value Propositionand learn more about what makes NC State the best place to learn and work for everyone.
What we offer:* Medical, Dental, and Vision
- Flexible Spending Account
- Retirement Programs
- Disability Plans
- Life Insurance
- Accident Plan
- Paid Time Off and Other Leave Programs
- 12 Holidays Each Year
- Tuition and Academic Assistance
- And so much more!
Attain Work\-life balance with our Childcare benefits, Wellness \& Recreation Membership, and Wellness Programs that aim to build a thriving wolfpack community.
*Disclaimer: Perks and Benefit eligibility is based on Part\-Time or Full\-Time Employment status. Eligibility and Employer Sponsored Plans can be found within each of the links offered.*
Essential Job Duties
The Data Science Librarian provides expert consultative support, workshop\-based training, and course\-integrated instruction for students, faculty, and researchers in the areas of data science and visualization. The position leads DSS’s support around tabular data, spreadsheet software and tools, and data visualizations, while also contributing to other data science topics aligned with their skills and patron needs. They design, develop, and deliver workshops, both as part of an open workshop series and in collaboration with faculty for course\-integrated instruction. They engage with faculty, campus groups, and external partners to support the university’s data and visualization needs, and conduct relevant outreach and participate in sponsoring events. The position collaborates with colleagues and faculty on data\-related initiatives and projects, contributes to departmental projects and initiatives, and may mentor or supervise graduate student consultants in their skill areas.
The position deeply considers how their approach to their daily work reflects and advances the Libraries’ Strategic Plan, creating an environment and community that is welcoming for both patrons and colleagues alike. The position participates in library planning and serves on library\-wide committees, task forces, and teams. Librarians are expected to be active professionally and to contribute to developments in the field. This position reports to the Department Head, Data Science Services.
This position is based in North Carolina. This position is eligible for flexible hours with a hybrid work environment. This position will require frequent onsite work.
NC State promotes an integrated approach to problem solving that transforms lives and provides leadership for social, economic, and technological development across North Carolina and around the world. NC State’s land grant mission of teaching, research and service is dedicated to the service of North Carolina and its people. Applicants are encouraged to review the institution’s mission, vision and strategic plan, and consider how their background, interest and experience would enable them to support the university.
Other Responsibilities
Other duties as assigned.
Qualifications
Minimum Education and Experience
- ALA\-accredited MLS, MIS, or other relevant, advanced degree
Other Required Qualifications
Applicants are encouraged to think broadly about and communicate how their skills and experiences transfer to the qualifications below, including in ways that may not be obvious, such as how non\-library\-specific skills and experiences speak to the position responsibilities. We strongly encourage and welcome applicants with a variety of backgrounds, interests, and experiences to apply for this position.
- Proficiency with concepts for working with tabular data, such as data generation, preparation, analysis, and demonstrated experience with one or more of the following tools for working with spreadsheets and tabular data, such as SPSS, SAS, JMP, GoogleSheets and/or STATA
- Proficiency with concepts for data visualization, including conceptualizing, designing, and building effective data visualizations for research presentations and publications
- Ability to teach workshops, instruction, or other types of training sessions about preparing and visualizing data, including sessions based in Google Sheets
- Demonstrated ability to learn and apply new technical skills and tools; evidence of ongoing, self\-directed learning
- Interest in providing services that support students, faculty, and staff in developing their data skills
- Ability to serve and collaborate with a large user community reflecting a variety of backgrounds and disciplines
- Ability to foster an environment of belonging and well\-being
- Ability to communicate clearly and knowledgeably through a variety of mediums; ability to represent the Libraries to university and external audiences
- Demonstrated ability to collaborate effectively and work in a team environment
- Evidence of ability for ongoing professional development and contribution to the library profession
Preferred Qualifications
It is not required that candidates meet any of the preferred qualifications to be considered for this position. The following skills represent some of the areas in which this position could potentially grow and focus, based on the incumbent’s background and interests.
- Demonstrated knowledge and evidence of application of one or more specific data approaches, such as: advanced data visualization; survey design and methodology; qualitative analysis; statistics; or others
- Proficiency with one or more programming languages for data transformation, analysis, and visualization, such as R, Python, etc.
- Experience providing consultation or developing and delivering instruction, workshops, or training for data topics
Required License(s) or Certification(s)
N/A
Valid NC Driver's License required No
Commercial Driver's License required No
Recruitment Dates and Special Instructions
Job Open Date 06/02/2026
Anticipated Close Date Open Until Filled
Special Instructions to Applicants
Please complete the required application fully and attach a cover letter, resume, and contact information for four (4\) current professional references.
Position Details
Position Number 00112010
Position Type Non\-Tenure\-Track Faculty
Full Time Equivalent (FTE) (1\.0 \= 40 hours/week) 1\.0
Appointment
Mandatory Designation \- Adverse Weather Non Mandatory \- Adverse Weather
Mandatory Designation \- Emergency Events Non Mandatory \- Emergency Event
Department ID 250101 \- Libraries
EEO
NC State University is an equal opportunity employer. All qualified applicants will receive equal opportunities for employment without regard to age, color, disability, gender identity, genetic information, national origin, race, religion, sex (including pregnancy), sexual orientation, and veteran status. The University encourages all qualified applicants, including protected veterans and individuals with disabilities, to apply. Individuals with disabilities requiring disability\-related accommodations in the application and interview process are welcome to contact 919\-513\-0574 to speak with a representative of the Office of Equal Opportunity.
If you have general questions about the application process, you may contact Human Resources at (919\) 515\-2135 or [email protected].
Final candidates are subject to criminal \& sex offender background checks. Some vacancies also require credit or motor vehicle checks. Degree(s) must be obtained prior to start date in order to meet qualifications and receive credit.
NC State University 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.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At North Carolina State 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 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
North Carolina State University AI Hiring
North Carolina State University 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% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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