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About This Role
Learning Technologist and AI Coordinator
U24: $76,486.40 - $122,359.00 (Salaries for internal candidates are determined by a promotional formula. Salaries for external candidates are determined up to step 4: $87,954.55, or dependent on qualifications)
Learning Technology, Innovation, and Services
102 West State Street Trenton, NJ 08608
Thomas Edison State University was founded in 1972 to break down barriers to degree completion. Fifty years later, it proudly serves a diverse body of adult students with innovative and professionally relevant academic programming in a predominantly online delivery. Our team of high-energy student-focused professionals is welcoming new members who are making their mark in education and are ready to help change the lives of thousands of adult students across New Jersey and around the world.
Candidates who are excited by changing lives, committed to the success of all and want to be challenged by new and innovative opportunities, should submit an application.
Summary:
The Learning Technologist and Artificial Intelligence Coordinator will play a key role in shaping engaging, accessible, and future-ready online learning experiences. This position brings together Canvas LMS expertise, instructional technology support, and the responsible use of generative AI to help course design and development teams work more efficiently and create stronger learning experiences for adult learners. Working in partnership with technology teams and academic leadership, this role will support high-quality course delivery, streamline digital learning workflows, and help guide the ethical, secure, and academically responsible use of AI across instructional and operational practices.
AI-Assisted Course Design and Development
- AI-Assisted Course Design and Development: Collaborate with instructional designers, assessment developers, and subject matter experts to develop prompts, templates, AI agents, and generative AI-supported materials that accelerate the creation of syllabi, learning objectives, active learning scenarios, case studies, and accessible course assets.
- Workflow Automation and Operational Efficiency: Use AI coding assistants, lightweight scripting, and automation tools to streamline repetitive instructional design, assessment, and administrative tasks across projects.
- Responsible AI Adoption and Governance: Support the integration of AI tools into institutional operations and course development in alignment with TESU strategic priorities, data governance policies, privacy, security, compliance, bias mitigation, and responsible-use expectations.
- AI Ethics and Academic Integrity: Guide course design and development teams in applying institutional AI policies, promoting ethical AI use, and supporting academic integrity through clear expectations, responsible practices, and appropriate use of AI tools.
- Research and Continuous Improvement: Monitor emerging AI trends, research, and best practices in online learning to inform course design, operational improvements, and institutional AI adoption.
Canvas Operations & Support* Canvas LMS Proficiency: Demonstrated ability to configure and maintain course spaces, assist users with basic system administration and troubleshooting, and manage common third-party tool integrations (LTIs).
- Support Canvas administrative operations, including feature integrations, LTI tools, permission structures, and troubleshooting for third-party software.
- Implement Course Revisions & New Builds: Partner with Instructional Designers to build, configure, and launch completed course revisions and new course developments within Canvas, ensuring precise structural setup, tool alignment, and adherence to institutional design standards.
- Resolve Technical Issues & Provide User Support: Serve as a primary point of contact for routine Canvas system administration, providing timely troubleshooting, issue resolution, and hands-on guidance for staff.
- Build AI-Ready Knowledge Base & Technical Documentation: Author, maintain, and structure comprehensive knowledge articles and process guides for learning technologies, optimizing content specifically for integration with Zendesk AI tools to drive self-service resolution and streamline user support.
- Champion Universal Design for Learning (UDL) principles and web accessibility standards (WCAG 2.2) across all digital course builds.
Additional Competencies:
- Consultative Guidance & Advisory: Ability to translate technical concepts into clear, encouraging language for instructors with varying levels of tech proficiency.
- Adult Learning Focus: Deep understanding of the unique motivations, self-directed nature, and practical needs of adult learners in digital spaces.
- Agility & Continuous Curiosity: A proactive drive to stay ahead of EdTech and AI developments, continuously testing, evaluating, and adapting emerging technologies.
- Cross-Functional Collaboration: Strong interpersonal skills to build trusting relationships across instructional design teams, IT departments, and academic leadership.
- Project Management & Operational Agility: Demonstrates the ability to independently organize, drive, and execute complex learning technology initiatives from inception through completion within a fast-paced higher education environment. Adept at managing multiple competing priorities, navigating academic governance, and consistently meeting tight, fixed deadlines (e.g., term starts, platform rollouts, staff training cycles) without compromising quality or user experience.
Requirements:
Education: Graduation from an accredited college with a Bachelor's degree supplemented by a Master's degree in a field related to the position to be filled or an equivalency as determined by the appointing authority.
Experience: Three years professional experience in a field that is directly related to the functions of the position to be filled or equivalency as determined by the appointing authority
- Applicants who do not possess the required education may substitute required experience on a year-for-year basis (30 credit hours is considered one year of college).
- A Ph.D. or an Ed.D. degree may be substituted for two years of the required experience.
Preferred Requirements:
- Canvas LMS Proficiency: Demonstrated ability to configure and maintain course spaces, assist users with basic system administration and troubleshooting, and manage common third-party tool integrations (LTIs).
- Higher Education Track Record: Proven experience working in a Higher Education setting, specifically supporting online course development and adult learning programs.
- Hands-On AI Experience: Demonstrated background using Generative AI tools (e.g., ChatGPT, Claude, Midjourney, Copilot) within instructional design or educational context workflows.
- Master’s degree in educational technology, or a closely related field.
- Core LMS Expertise: Advanced to expert-level experience in Canvas LMS administration, course architecture, LTI integrations, and technical troubleshooting.
- AI Policy: Experience contributing to institutional AI governance, academic integrity guidelines, or AI pilot program evaluations.
- Experience using agentic AI coding tools (such as ChatGPT Codex, Claude Code) to build lightweight automations, scripts, or internal utilities that support teaching, learning, or academic operations systems.
- Understanding of how API-based integrations (e.g., LMS, SIS, or ERP systems such as Ellucian Colleague) and emerging AI integration standards (such as MCP) support automation and AI-enabled workflows in an academic environment
- TESU strives to offer a flexible work environment while balancing the operational needs of the University. As such, we have several options for hybrid-work schedules. Positions determine what type of hybrid-work schedule is available and, if necessary, adjustments are made to support University operations.
Thomas Edison State University is an Equal Opportunity/Affirmative Action Employer
Salary Context
This $76K-$122K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Thomas Edison 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($99K) sits 53% below the category median. Disclosed range: $76K to $122K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Thomas Edison State University AI Hiring
Thomas Edison State University has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Trenton, NJ, US. Compensation range: $122K - $122K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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