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About This Role
General Summary
The Office of Institutional Research (IR) is housed within Academic Operations and is involved in conducting, managing and implementing institutional research and assessment related activities. It performs all necessary functions related to the collection, interpretation, and use of data for planning and decision making across the university system.
The Director of Institutional Research supervises all institutional research activities and directs and manages data collection, compilation, analysis, and research activities. Provides leadership and direction for the design, prioritization, and implementation of statistical analysis and research activities in support of assessments, program review s and continuous quality improvements. Develops and executes plans to support outcome assessments, program and department reviews, continuous improvement, and institutional effectiveness activities.
Principal Duties \& Responsibilities
- Ensure that meaningful, appropriate and accurate data and supporting documentation is available to meet the decision\- making needs of departments and programs across the system
- Collect, interpret, and analyze data, to make recommendations based on results for different activities and projects
- Run appropriate statistical tests on data. Ensure reliability and validity of data
- Produce appropriate reports regarding the achievement of program or department performance measures and outcomes to appropriate parties
- Support academic program and course review activities. Provide support with data, analyses and recommendations for program and course health reviews
- Develop, coordinate and publish data reports
- Research and recommend changes in institutional processes to improve data quality and accuracy
- Compile, analyze and disseminate quantitative and qualitative data on facets of the programs and departments. Support and document the integration of the results of these reviews into decision\-making and planning
- Collect and maintain college/university/system\-wide assessment results, which include analyzing and interpreting data for informed decision making and reporting performance results
- Oversee the design and analysis of surveys and focus groups of various constituencies, communicate the results of analyses to appropriate audiences and support and document the integration of the results into decision\-making, continuous improvements, and planning
- Stay abreast of data needs related to accreditation; support accreditation efforts by providing data and analyses for use in self\-studies and reports
- Evaluate the information needs across the system, and oversee the data to support institutional research and provide necessary information
- Collaborate with other departments (e.g., IT, data teams) in the development of dashboards and integrated data collection tools
- Manage responses to ad\-hoc requests for data, analyses, and reports
- Manage a team of direct reports to support the work of the department
Other Duties as Assigned or Requested
- Special projects based on business needs
Qualifications:
Education \& Experience (minimum)
- Doctorate in the social, psychological, educational, or research fields
- Five or more years of experience in institutional research or leading similar functions in a large organization
- Experience with data collection, analysis, interpretation, visualization, and reporting
- Knowledge of qualitative and quantitative research designs and analysis
- Understanding of methods of data management and analysis in a higher education environment
- Excellent project management skills
- Excellent written and oral communication skills
- Ability to work effectively with system, department and program leaders, faculty, administrative staff, and students
- Ability to work independently, attend to multiple projects simultaneously, and meet deadlines
- Experience with data analytics software applications, such as SPSS, Microsoft Excel, qualitative analysis tools and/or others
Education \& Experience (preferred)
- Advanced degree in psychometrics and/or research methodology
- Experience with Qualtrics Survey Tools
- Experience managing direct reports
- Experience working and leading remotely
Knowledge, Skills, Abilities \& Competencies
- Demonstrated understanding of higher education.
- Demonstrated understanding of applicable regulatory, accrediting agency, and professional association standards.
Competency – Drive for Results/Action Oriented
- Proven ability to establish and articulate a vision, set and exceed goals, develop and execute strategies, and track and measure results.
- Proven ability to build and motivate a team to achieve well\-communicated expectations, steadfastly pushes self and others for results.
- Proven ability to lead projects and achieve results in an ambiguous work environment.
Competency \- Execution
- Proven organizational ability, highly skilled at finding resources to get things done, and can orchestrate multiple activities at once to accomplish a goal.
- Skilled priority setting, allocates time of self and others on what's important; differentiates issues by relative importance and takes action accordingly to follow through.
- Track record of persevering despite challenges to overcome resistance and obstacles in pursuit of key objectives.
Competency – Functional/Technical Skills
- Extensive expertise in data collection, analysis, and reporting
- Extensive expertise in process implementation
- Experience conducting quantitative research and data analysis
- A solid understanding of qualitative research and data analysis
- Knowledge and experience conducting survey research
Competency – Business \& Strategic Acumen
- Must possess strong strategic and business acumen, for a complex business environment same or similar to proprietary education. Knows how businesses and educational institutions work, is up to date on trends, technology, and information, and is aware of how strategies and tactics work in the marketplace.
- Demonstrated ability to deal with concepts and complexity comfortably and is sharp, capable, and agile. Can connect the dots between disparate points and anticipate downstream consequences. Can create competitive and breakthrough strategies and plans.
- Makes decisions that are simultaneously well researched, thoughtful, and timely before exhausting too much time and resources. Demonstrated ability to make quick but well\-informed decisions.
- Demonstrated ability to use data to inform decisions.
Competency – Collaboration \& Teamwork
- Proven strong negotiating and consensus\-building abilities.
- Proven strong leadership skills within the team and in the academic community.
- Leads team by providing strong communication and respect. Provides valuable feedback and maintains composure at all times.
- Proven skills to work effectively across internal functional areas in ambiguous situations. Knows how organizations work and how to get things done both through formal channels and informal networks.
Competency \- Communication \& Composure
- Excellent written and verbal communication skills.
- Excellent presentation skills.
Req Benefits: Paid time off \* Paid sick leave \* Paid holidays \* Comprehensive medical, pharmaceutical, dental, and vision benefits \* Health savings and flexible spending accounts \* 401(k) savings plan with company match \* Employee Stock Purchase Plan (ESPP) \* Company paid life insurance and disability insurance \- subject to eligibility \* Company paid tuition assistance \- subject to eligibility and approval \* Employee Assistance Program (EAP) \* Prenatal and adoption assistance \* Additional ancillary programs are available upon benefit enrollment eligibility \*Most benefits apply to full\-time employees. Some benefits apply to part\-time employees as well. Benefits may vary by location and position and are subject to change at any time. Ask your recruiter for full details and information about eligible dependents.
Compensation: $99,800 to $110,000 Annually
Salary Context
This $99K-$110K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Perdoceo Education Corporation, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($104K) sits 37% below the category median. Disclosed range: $99K to $110K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Perdoceo Education Corporation AI Hiring
Perdoceo Education Corporation has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $56K - $110K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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