Interested in this AI/ML Engineer role at Arizona State University?
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About This Role
Job Profile:
Applications Developer 3 Job Family:
IT Applications Time Type:
Full time Apply before 11:59 PM Arizona time the day before the posted End Date.
Minimum Qualifications:
Bachelor's degree and five (5) 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:
Designs, develops, and troubleshoots moderately complex software and applications in support of establishing a sustainable institutional technological infrastructure. Job Description:
Enterprise Technology is building a new Agentic Operations team and is hiring two Senior Agentic Operations Engineers to help build this capability from the ground up. The team will develop and manage AI agents that monitor university systems, identify potential issues, and help resolve problems before they impact the ASU community.
As a member of this new team, you will help establish the tools, processes, and safeguards needed to use AI safely and effectively within ASU’s technology environment. You will work across monitoring, automation, security, and AI-driven remediation, with a focus on building reliable and well-governed solutions.
This is an opportunity to shape a new capability at ASU. We are looking for experienced engineers who are comfortable with change, learn quickly, and enjoy solving complex problems. As the platform matures, you will help expand the ways AI can safely support university operations.
Position Salary Range: $78,000.00 – $120,000.00 per year; DOE.
Essential Duties:
- Help build, deploy, and govern a platform of AI agents that monitor ASU's environment and, over time, help remediate issues safely.
- Contribute to the discovery and definition of the team's tooling, practices, standards, and operating model as a new group.
- Work across telemetry and detection (ingesting signals, modeling baseline health, tuning anomaly detection) and orchestration and remediation (secure execution, IAM/RBAC, human-audited runbooks, failsafe rollbacks), developing deeper emphasis in one area over time.
- Uphold and help define governance standards: role-based access control, audit logging, human-in-the-loop escalation, and reversible/rate-limited actions.
- Sequence automation responsibly up an autonomy ladder — starting with low-risk, reversible actions on low-blast-radius systems and earning broader autonomy as trust is proven.
- Serve as a peer reviewer for the other engineer's agent logic before any production deployment.
- Partner with system owners, security, and other teams to adopt agents safely across the environment.
- Leverage AI in routine tasks and contribute ideas for using AI to improve the unit's efficiency and overall performance.
- Take responsibility for fostering a positive culture, upholding organizational values, and championing engagement in all its forms.
Collaborate across teams and actively participate in ET/ASU events and programs. *
Desired Qualifications:
- We do not expect every candidate to have all of the following. Strong generalists with depth in several of these areas are encouraged to apply.
- Hands-on experience with LLMs and/or agent-based systems and their operational risks.
- Strong programming skills in Python (async, typing, testing).
- Cloud infrastructure experience (AWS preferred) and infrastructure as code (OpenTofu/Terraform).
- Experience with observability and telemetry (metrics, logs, traces);
- familiarity with Datadog a plus.
- Experience in one or more of: time-series/anomaly detection or baseline modeling; or secure automation, IAM/RBAC, and safe automated remediation with rollback.
- Familiarity with SRE practices — SLOs, error budgets, incident management, on-call.
- Comfort operating in an ambiguous, discovery-oriented environment and helping define new practices.
- Demonstrated ability to model empathy, compassion, and emotional intelligence.
- Experience in a values-driven organization with a strong commitment to inclusion and belonging.
- Ability to cultivate a psychologically safe environment where all team members can thrive.
ABOR only: Evidence of current employment in an Arizona Board of Regents Organization; ASU, U of A, or NAU. *
Working Environment:
- Activities primarily take place in a standard, climate-controlled office environment and may involve extended periods of sitting, standing, or moving between office spaces.
This role involves regular use of a computer (desktop and/or laptop), including a keyboard and mouse. *
Primary Location: SkySong — Scottsdale, AZ. This is a hybrid position with an expectation of three (3) days per week on-site. Candidates must reside in, or be willing to relocate to, the greater Phoenix metropolitan area.
*Please submit a cover letter that addresses the following:
- Briefly explain your hands-on experience with LLMs/agentic systems and how you addressed operational risk or safety in those projects.
- How do you approach building and operating in an ambiguous, newly established team/environment?
- AI Disclosure: If you used generative AI tools to assist in drafting your application materials, please briefly disclose how you utilized them.
Department Statement:
Enterprise Technology (ET) embraces its role as both an enabler and catalyst for advancing the vision and work of the New American University. We are a values-driven organization. Our commitments are reflected in all of the work we do in pursuit of operational excellence, the experience and delight of our community, and our strategic and innovation initiatives. Applicants must be eligible to work in the United States.
Join the team that sparks human-centered innovation
ET is a rapidly reconfigurable and entrepreneurial organization at ASU that prioritizes and executes to meet the needs of our community of learners, faculty, researchers and staff. Our work emphasizes autonomy, flexibility and distributed decision-making to leverage the strengths of individuals. Together, we embrace a culture that nurtures, engages and embraces many voices with a shared lens of positive community impact and expanded opportunities for collaboration.
Why join us?
Mission oriented. Everything we do is to advance ASU’s charter - measuring who we include and how they succeed. We are staunch champions of learner success and put people first.
Flexibility. Our flexible work environment allows employees to explore opportunities with their supervisor beyond the traditional work schedule. Opportunities include hybrid work where staff are in person three days per week.
Culture forward. We embrace a Positive Core culture: Belonging, Relational, Authentic, Visionary and Empowered.
Scale of impact. Our work changes the world. With 200k+ learners, faculty, researchers and staff, working with ET means you have the capacity to improve many lives and entire communities.
World-class, low cost education. Our professional development is built in! ET encourages staff to seek additional certificates and degrees via ASU’s top ranked programs with major tuition breaks.
Exposure to industry giants. ET partners with Amazon, Apple, Arista, Cox, Verizon, Salesforce, Alteryx and a diversity of technology companies to enhance our innovations and deepen their impact.
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 $78K-$120K 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($99K) sits 53% below the category median. Disclosed range: $78K to $120K.
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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