Interested in this AI/ML Engineer role at Perdoceo Education Corporation?
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About This Role
The Senior National Admissions Advisor, consults with prospective students with respect to their decision to attend college/university. The Senior National Admissions Advisor delivers a high level of service to students throughout their first session. In addition to communicating the philosophy and features of the school and serving as an advocate for prospective students, the Senior National Admissions Advisor must meet the school's quantitative and qualitative goals and objectives, as established, in an accurate, professional, and compliant/ethical manner using school\-approved admissions processes and procedures.
Principal Duties \& Responsibilities
- Responsible for developing and meeting action plans in order to obtain qualitative and quantitative goals and objectives.
- Evaluate monthly activity against standards.
- Professionally assists prospective students through the admissions process in accordance with all applicable federal and state regulations, school policies and procedures, and in compliance with all accrediting standards and requirements.
- Partner with prospective students via telephone interviews and/or e\-mail correspondence to determine educational needs, concerns and interests.
- Increased focus on student service by way of additional time on queue to assist with requested information.
- Develop a rapport with prospective students and maintain contact with them frequently.
- Partners with departments outside of Admissions, including Financial Aid and Academics, to ensure the delivery of a high level of service to every student.
- Provide support to enrolled students to make sure they have submitted all documents needed to begin coursework.
- Support students through the orientation process to ensure adaptation of the virtual/in person campus.
- Encourage and guide students to utilize the virtual classroom preparatory course.
- Ensure adequate use by students in the institution's Ready course.
- Work with lead faculty to support students in submitting assignments during their first session.
- Consistently operates within, and is measured with respect to, the school's expected behaviors: Execution, Communication/Feedback and Professionalism.
- Ensure compliance with applicable University policies and procedures.
Other Duties as Assigned or Requested
- Willingness to work nights, weekends, and holidays.
- Ability to work in a fast\-paced environment
- Ability to remain flexible and easily adapt to changes in work environment or schedule.
- Advisors provide a positive, professional impression for students and staff on the phone and via email.
- Ability to handle multiple tasks and meet deadlines as well as communicate effectively with individuals from diverse backgrounds.
- Ability to achieve success individually and as part of a team in a highly structured and regulated work environment
Knowledge, Skills and Abilities, Competencies
Basic computer skills * \- experience with Microsoft (WORD, Excel, Outlook) preferred
Possesses excellent verbal communication skills, particularly telephonic * \- and strong written communication skills
Persistence combined with a positive attitude and approach to work and others * \- self\-motivating work style
- Positive and collaborative interpersonal skills; willing to assist others
- Adaptability to quickly learn to effectively utilize all applicable school systems, databases and tools.
Education and Experience: Minimum
- High School Diplomaor its equivalent required
- 2\-5 Years of experience in sales and/or customer service
Education and Experience: Preferred
- College degree
- Post\-secondary Higher Education admissions experience
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: $25\.00 \- $27\.88 hourly
Salary Context
This $52K-$56K range is below 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($54K) sits 68% below the category median. Disclosed range: $52K to $56K.
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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