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Posting Summary
Working Title Project Coordinator\-AI\-Enabled Research and Pedagogical Innovation
Role Title Education Support Spec III
Role Code 29144\-FP
FLSA Nonexempt
Pay Band 04
Position Number 280W0863
Agency Northern VA Community College
Division NV280\-Exec VP Academic \& Student Svs
Work Location Fairfax County \- 059
Hiring Range $35/hr
Emergency/Essential Personnel No
EEO Category 2\-Professionals
Full Time or Part Time Part Time
Does this position have telework options? \-Telework options are subject to change based on business needs\- No
Does this position have a bilingual or multilingual skill requirement or preference?
Work Schedule
Flexible: 20\-29 hours per week depending on program needs; Monday through Friday between 7 am and 7 pm EST. Part\-time, project\-dependent position. Due to the cyclical nature of the project timelines, hours will fluctuate, and there will be periods with no scheduled work between active project phases.
Sensitive Position No
Job Description
General Description:
The Project Coordinator\-AI\-Enabled Research and Pedagogical Innovation works with the Director of Faculty Professional Development to design and implement a comprehensive research and assessment plan within the Center for the Advancement of Teaching Excellence (CATE). The primary responsibility is to design and implement rigorous research plans, enhanced through the use of AI, that systematically assess the impact of current faculty professional development initiatives. Additionally, the role may include development and facilitation of faculty professional development in emerging technologies (e.g., GenAI) as well as evidence\-based pedagogical strategies (e.g. TAD/TILT).
Duties and Tasks:
- Program \& Data Review: Evaluate existing professional development programs, materials, and participant data.
- Research: Conduct research on relevant topics to guide project development.
- Assessment Instrument Design: Construct assessment tools, including surveys, interview protocols, and focus group guides.
- Data Collection \& Collaboration: Partner with CATE and other college staff members to gather data from faculty participants.
- Data Analysis: Use AI to perform and validate comprehensive qualitative and quantitative data analysis on collected feedback.
- Reporting \& Presentation: Synthesize findings into formal reports with actionable recommendations.
- Professional Development: Utilize train\-the\-trainer style methodology to train CATE staff and relevant college units to use AI for research design, data analysis, and human\-in\-the\-loop validation.
- Content Development: Create faculty professional development materials in emerging technologies (e.g. GenAI) as well as evidence\-based pedagogical strategies (e.g. TAD/TILT).
Special Assignments
May be required to perform other duties as assigned. May be required to assist the agency or state government generally in the event of an emergency declaration by the Governor.
KSA's/Required Qualifications
KSAs:* Demonstrated understanding of high\-impact, evidence\-backed, student\-centered pedagogies within higher education contexts, including the integration of emerging technologies like GenAI into teaching practices.
- Demonstrated understanding of instructional design principles, educational research methodologies, and both qualitative and quantitative data analysis.
- Ability to translate complex data sets and academic literature into actionable insights, clear reports, and engaging stakeholder presentations.
- Ability to work productively alongside departmental staff, faculty members, and institutional stakeholders to achieve project goals.
- Ability to apply logical sequences to analyze issues, resolve systemic challenges, and exercise independent, sound judgment.
- Ability to work autonomously with minimal supervision whilst acting as a collegial, effective member of a diverse team.
- Ability to effectively train, upskill, and support CATE staff, faculty, and college units on the technical application of AI tools in research and instructional contexts.
- Solid knowledge of standard business protocol, professional office systems, and administrative procedures.
Minimum Work Experience:* Experience in leveraging Artificial Intelligence and generative AI tools as an advanced methodology for research design, qualitative/quantitative data analysis, and human\-in\-the\-loop validation.
- Experience in train\-the\-trainer style methodology to train others in use of AI for research design, data analysis, and human\-in\-the\-loop validation.
- Experience in creating faculty professional development materials in emerging technologies (e.g. GenAI) as well as evidence\-based pedagogical strategies (e.g. TAD/TILT).
Additional Considerations
- Experience with Canvas or other Learning Management Systems (LMS), Zoom or other web\-based conferencing platforms, and PeopleSoft or other Student Information Systems (SIS).
- Experience in developing educational evaluation metrics and assessment tools, such as surveys, focus group guides, and interview protocols.
- Experience in word processing, database, spreadsheet, and multimedia software, including the Microsoft Office and Google suites.
Operation of a State Vehicle No
Supervises Employees No
Required Travel
Maybe required to travel between NOVA campuses
Posting Detail Information
Posting Number WGE\_3307P
Recruitment Type General Public \- G
Number of Vacancies 1
Position End Date (if temporary)
Job Open Date 07/23/2026
Job Close Date 08/09/2026
Open Until Filled No
Agency Website
Contact Name
Phone Number
Special Instructions to Applicants
In support of the Commonwealth’s commitment to inclusion, we are encouraging individuals with disabilities to apply through the Commonwealth’s Alternative Hiring Process. To be considered for this opportunity, applicants will need to provide their AHP Letter (formerly called a Certificate of Disability) provided by the Department for Aging \& Rehabilitative Services (DARS), or the Department for the Blind \& Vision Impaired (DBVI). Service\-Connected Veterans may also apply via the AHP if they also provide an AHP Letter. To request an AHP Letter, use this link:
Additional Information
Northern Virginia Community College (NOVA) is the largest public institution of higher education in the Commonwealth of Virginia and one of the largest community colleges in the nation. NOVA enrolls nearly 75,000 students on its six campuses in Alexandria, Annandale, Sterling, Manassas, Springfield, and Woodbridge, as well as through NOVA Online and high school dual enrollment programs. The College offers more than 100 affordable associate degree and certificate programs to help our students reach their academic and professional goals in some of the most in\-demand careers.
At NOVA, we are deeply committed to fostering an inclusive community for all students, faculty, and staff, and our diverse workforce is representative of this commitment. To this end, we encourage all applicants seeking to add value through their diverse backgrounds, experiences, and interests to consider employment opportunities with NOVA. To learn more about NOVA’s commitment to inclusive excellence, please visit At a Glance \| Northern Virginia Community College.
NOVA offers eligible employees a benefits package that includes a comprehensive health and dental insurance program, generous paid leave, deferred compensation plans, paid parental leave, state employee discounts, and a solid and secure retirement program. We strive to ensure our employees have tools and development opportunities to support and promote NOVA’s mission. For more information about NOVA and its programs and services, please visit our website at
Employees must reside in Virginia, Maryland or District of Columbia
Advertising Summary \|
Background Check Statement Disclaimer
The selected candidate’s offer is contingent upon the successful completion of a criminal background investigation, which may include: fingerprint checks, local agency checks, employment verification, verification of education, credit checks (relevant to employment). Additionally, selected candidates may be required to complete the Commonwealth’s Statement of Economic Interest. For more information, please follow this link: http://ethics.dls.virginia.gov/
EEO Statement
The Virginia Community College System (VCCS) provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, political affiliation, veteran status, sexual orientation, gender identity or other non\-merit factors.
ADA Statement
The Virginia Community College System (VCCS) is an Equal Employment Opportunity employer and complies with the Americans with Disabilities Acts (ADA and ADAAA), to provide, reasonable accommodation to applicants in need of access to the application, interviewing, and selection processes when requested.
E\-Verify Statement
VCCS uses E\-Verify to check employee eligibility to work in the United States. You will be required to complete an I\-9 form and provide documentation of your identity for employment purposes.
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 Virginia Community College System, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Virginia Community College System AI Hiring
Virginia Community College System has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in VA, 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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