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About This Role
Job Profile:
IT Project Specialist 2 Job Family:
IT Project Management Time Type:
Full time Apply before 11:59 PM Arizona time the day before the posted End Date.
Minimum Qualifications:
Bachelor's degree and three (3) years of experience appropriate to the area of assignment/field; OR, Any equivalent combination of experience and/or training from which comparable knowledge, skills and abilities have been achieved.
Job profile Summary:
Works closely and collaboratively with project managers and product managers to assist with routine tactical tasks for project/product execution; develops and maintains relationships with the project/product team members; is committed to project/product success. Job Description:
Do you want to build AI tools that change how an entire organization creates content? Are you energized by the space between content strategy, technology and product, where prompts, structured knowledge and workflows come together to create something people use every day? Then keep reading below for this exciting AI Content Strategist opportunity!
ASU EdPlus is a dynamic unit of Arizona State University focused on the design and scalable delivery of digital teaching and learning models to increase student success and reduce barriers to achievement in higher education. We advance the economic, social, cultural, and overall health of the local, national, and international communities served by ASU.
Want to know more about working at EdPlus? Watch this short video about working at EdPlus!
We are seeking an AI Content Strategist to join the AI Content Strategy and Systems team. Every day you will make a difference in the lives of others by designing, building and scaling custom AI content tools and agentic workflows that help EdPlus teams create brand-aligned, student-centered content at scale. You will help define what responsible AI content creation looks like at EdPlus and turn those standards into skills, custom assistants, structured knowledge, editorial guidance and automated workflows that support quality content creation across the organization.
This is a hands-on AI content strategy role. You’ll own discovery, design, testing, iteration and enablement for the AI content tools you build, treating them as internal products with real users and measurable adoption. You do not need an engineering or product background, but you do need to be comfortable building directly in AI platforms through instructions, configuration, structured knowledge and repeated testing. You’ll also serve as the editorial owner for system outputs: editing democratized content, identifying where the model falls short, improving the tools and ensuring anything that reaches students is engaging, accessible and unmistakably ASU.
We’re looking for a strong content strategist with experience in UX content, product writing or digital content strategy who has been experimenting with AI tools and is ready to specialize. You’ll bring excellent editorial judgment, curiosity about how these systems work, strong collaboration skills and a passion for building user-friendly products. If you know good content when you see it and can’t resist experimenting with new technologies, then this position might be the right fit for you.
NOTE: This is an in-person, hybrid position. You must be able to reliably commute to Scottsdale AZ.
Essential Duties:
- Own custom AI content tools and agentic content systems across EdPlus, from the roadmap, strategy and design through building, testing, launching, and iterating.
- Build, configure and maintain custom AI content tools in platforms such as Claude and ChatGPT. This includes skills, Custom GPTs, project-based workspaces, task-specific subagents, automated workflows, evaluation sets and prompt libraries.
- Own editorial quality for democratized content, reviewing and editing outputs for brand voice, clarity, best practices and student impact, and diagnosing quality issues to improve the instructions, context and source content in our AI content tools.
- Conduct discovery with EdPlus teams to understand current workflows, identify high-value AI content use cases and gather ongoing feedback to maintain a prioritized backlog and roadmap based on impact and feasibility.
- Partner with content strategy colleagues to translate messaging frameworks, brand voice, and editorial standards into the instructions, context, and knowledge AI tools rely on, ensuring the tools produce high-quality content that reinforces ASU Online's content strategies.
- Partner with technology, product and AI teams on integrations, technical requirements, platform decisions and shared standards.
- Define and track success metrics for AI content tools, including adoption, usage, output quality, time saved and downstream content performance.
- Drive adoption of AI content tools across EdPlus through onboarding, training, office hours and ongoing enablement, including documenting how the tools work and how to get the best results from them.
- Define and operationalize governance for AI content creation, including review requirements, disclosure standards and guardrails for appropriate use.
- Evaluate emerging AI tools, models and capabilities through structured pilots, recommending what to adopt, build or pass on, and contribute to the long-term vision for how content, AI and systems come together at EdPlus.
Assume or coordinate additional duties or projects as assigned or directed. *
NOTE: This is an in-person, hybrid position. You must be able to reliably commute to Scottsdale AZ.
Desired Qualifications:
- Bachelor's degree or higher in communication, marketing, UX, information science, product management or related discipline.
- Demonstrated experience in content strategy, UX content or technical content, including strong editorial judgment and excellent writing skills for digital products and experiences; demonstrated ability to hold a consistent quality bar across high volumes of content and to articulate the reasoning behind editorial decisions.
- Experience applying messaging frameworks, brand voice and style standards consistently across channels, formats and multiple contributors, with demonstrated strong understanding of brand governance, accessibility and inclusive content practices.
- Hands-on experience building with generative AI , such as designing prompts, custom assistants, agents or AI-powered workflows . (You do not need to have shipped a formal AI product, but you should be able to show us what you have built, tested and learned from experimenting with AI in your content work.) Experience with APIs or no-code/low-code automation platforms is a plus but not required.
- Demonstrated ability to define requirements, prioritize a roadmap and iterate on a tool or product based on user needs and feedback.
- Evidence of familiarity with structured content, knowledge bases, retrieval-based approaches and how content structure affects AI output quality.
- Evidence of excellent collaboration and communication skills, including the ability to work across content, technology, product and marketing teams; explain technical concepts to non-technical audiences; and collaborate credibly with technical partners.
A portfolio showing your writing samples and work with AI may be required. *
NOTE: Please answer the following questions in your cover letter:
- Share something you built because you wanted it to exist, such as a custom AI assistant, skill or automated workflow. What did you build and how did you improve it over time?
How do you decide whether AI-generated content is good enough to publish? Walk us through your review process and tell us how you hold the line when needed. *
Salary & Benefits:
$71,500 – $88,000 per year; DOE
ASU offers a total compensation package that includes valuable employee benefits. See the ASU Benefits website to explore options.
- Healthcare
- Financial Security
- Retirement
- Family Resources
- Tuition Reduction (Eligible ASU Employee, their dependents and spouse.)
Discounts *
Working Environment
Your desk will be in the beautiful, cutting-edge, and collaborative workspace at SkySong, the ASU Scottsdale Innovation Center. The Center houses a diverse business community that links technology, research, education, and entrepreneurship to position ASU and Greater Phoenix as global leaders in the knowledge economy.
ASU EdPlus supports flexible work options, ranging from alternate to hybrid work schedules, subject to approvals per ASU policy. (This is an in-person, hybrid position.)
Applicant must be eligible to work in the United States. ASU EdPlus will not be a sponsor for this position.
Department Statement:
As a central enterprise unit for ASU, ASU EdPlus supports the university charter by focusing on the design and scalable delivery of digital teaching and learning models to increase student success and reduce barriers to achievement in higher education.
ASU EdPlus defines itself through a culture of curiosity, risk-taking, and refusing to accept the status quo. Our employees are valued, respected, and encouraged to be their unique selves. We know that our ability to deliver high-quality services and educational experiences is strengthened by our culture of innovation, driving outcomes through serving learners, achieving milestones, striving for excellence, solving problems, embracing urgency, and being bold.
ASU EdPlus
Driving Requirement:
Driving is not required for this position. Location:
Off-Campus: Scottsdale Funding:
No Federal Funding
Instructions to Apply:
Current employees, student workers seeking staff opportunities, and students applying for student worker positions must apply directly through the Workday Jobs Hub .
To be considered, your application must include all of the following attachments:
- Cover letter
Resume or CV *
Multiple documents may be uploaded in the attachments section. Alternatively, applicants may combine all required materials into a single PDF for submission. Please ensure uploaded documents are clearly labeled and include your name.
Please ensure your resume includes all employment information in month and year format, for example 6/04 to 8/14, along with job title, job duties, and employer name for each position. Your resume should clearly demonstrate how your experience and background meet the minimum and desired qualifications for this position. Incomplete applications or missing required materials may not be considered.
Important: Do not withdraw your application to make edits. Once an application is withdrawn, it cannot be edited, reactivated, or replaced with a new submission. If you have questions or need assistance, please contact The Office of Human Resources Talent Acquisition before the posting close date.
Graduate Assistant, Intern and part-time positions are counted as half time for experience equivalency, meaning one year equals six months of experience.
Only electronic applications will be accepted for this position. By submitting an application, you confirm that the information provided is accurate and complete.
ASU Statement:
Arizona State University is a new model for American higher education, an unprecedented combination of academic excellence, entrepreneurial energy and broad access. This New American University is a single, unified institution comprising four differentiated campuses positively impacting the economic, social, cultural and environmental health of the communities it serves. Its research is inspired by real world application blurring the boundaries that traditionally separate academic disciplines. ASU serves more than 100,000 students in metropolitan Phoenix, Arizona, the nation's fifth largest city. ASU champions inclusive excellence, and welcomes students from all fifty states and more than one hundred nations across the globe.
ASU is a tobacco-free university. For details visit https://wellness.asu.edu/explore-wellness/body/alcohol-and-drugs/tobacco
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other basis protected by law.
Notice of Availability of the ASU Annual Security and Fire Safety Report:
In compliance with federal law, ASU prepares an annual report on campus security and fire safety programs and resources. ASU’s Annual Security and Fire Safety Report is available online at https://www.asu.edu/police/PDFs/ASU-Clery-Report.pdf . You may request a hard copy of the report by contacting the ASU Police Department at 480-965-3456.
Relocation Assistance – For information about schools, housing child resources, neighborhoods, hospitals, community events, and taxes, visit https://cfo.asu.edu/az-resources .
ASU’s comprehensive benefits package is a key part of your total compensation. Visit the ASU Compensation Estimator
Employment Verification Statement:
ASU conducts pre-employment screening which may include verification of work history, academic credentials, licenses, and certifications.
Background Check Statement:
ASU conducts pre-employment screening for all positions which includes a criminal background check, verification of work history, academic credentials, licenses, and certifications. Employment is contingent upon successful passing of the background check.
Fingerprint Check Statement:
This position is considered safety/security sensitive and will include a fingerprint check. Employment is contingent upon successful passing of the fingerprint check.
Salary Context
This $71K-$88K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Arizona 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($79K) sits 62% below the category median. Disclosed range: $71K to $88K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Arizona State University AI Hiring
Arizona State University has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Scottsdale, AZ, US. Compensation range: $88K - $120K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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