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About This Role
Reporting to the Faculty Lead (or Program Director), the Part\-Time Faculty member teaches and engages students within a particular discipline for which they are qualified. This position is critical in providing a quality education for students and ensuring their persistence towards the completion of their degree plans.
Essential Functions: Instruction and Engagement
- Teach Students and Assess Their Work in the Trident Learning Community (TLC):
+ Convey command of and enthusiasm for the course content by providing insight and sharing expertise with the class throughout the course, e.g., through timely posts to News You Can Use and/or the class discussions.
+ Use the grading rubrics to provide detailed feedback on student assignments based upon the assignment criteria (i.e. substantive comments explaining what the student did well, why any points were taken off, and how to improve substantively).
+ Broaden and deepen students' understanding of the course material by posting prompts and responses in the course discussions.
- Engage Students Regularly and in a Timely Fashion:
+ Communicate with the class as a whole at the beginning of each module to: (a) engage students in the course materials and the class, (b) provide direction, (c) reflect on the course, (d) remind students of tasks and due dates, (e) communicate changes, etc.
+ Welcome all students, encouraging them to get started and provide initial guidance.
+ Create a welcoming and comfortable environment and maintains a professional, positive, and respectful tone through multiple communication channels.
+ Respond to communications received from students within 24 hours during the business week.
+ Grade students' assignments within 72 hours of their submission during the business week (including assignments submitted by students with approved course extensions).
+ Reach out and offer to help students who have not logged into the course within the first few days as well as to those who are falling behind and/or struggling with assignments.
+ In the course discussions, post and respond regularly to guide the discussion and provide feedback to the class through the techniques of summarizing, reflecting, encouraging, and identifying excellence.
- Administer:
+ Perform routine duties in a timely fashion, without prompting or reminders, in the courses you are assigned to teach. These duties include but are not limited to:
+ Respond to e\-mail and/or phone correspondence from your Faculty Lead and/or Program Director within 24 hours during the business week. Let them know in advance when circumstances arise that make it difficult to respond promptly.
+ Work closely with your Faculty Lead and/or Program Director to:
- Resolve student academic questions, complaints, and concerns in a timely manner, consistent with the university catalog.
- Enforce academic policies as detailed in the current university catalog.
- Implement college and/or program\-specific procedures and protocols through communications with the class as a whole as well as with individual students.
+ Review and approve requests for regular course extensions that students in your course(s) submit through the TLC portal.
+ Grade assignments submitted by students with approved course extensions in a timely manner and confirm their final course grades in the TLC portal.
+ Report any basic issues with the course materials (e.g. broken and/or missing links and/or materials) to your Faculty Lead or Program Director immediately.
+ Actively participate in:
- Student retention efforts.
- Periodic asynchronous faculty development activities.
+ Encouraged (but not required) to:
- Provide substantive feedback to improve course content and the curriculum.
- Participate in periodic Program, College, and University meetings.
Knowledge, Skills and Abilities, Competencies
- Ability to inspire and engage students by delivering quality instruction and support.
- Ability to demonstrate academic rigor and fairness in grading.
- Ability to provide constructive feedback.
- Outstanding interpersonal skills with ability to create and sustain a collegial, collaborative, team\- oriented environment.
- Demonstrated ability to communicate effectively, both orally and in writing.
- Excellent analytical and problem solving skills with ability to formulate feasible and logical solutions.
- Proficient in Microsoft Office applications with ability to quickly learn new technologies and software.
Education and Experience: Minimum
- To teach at the graduate level, a terminal degree from a regionally accredited institution in a relevant discipline is required
- To teach at the undergraduate level, a terminal degree from a regionally accredited institution in a relevant discipline is required
- Experience teaching in a higher education setting
- Demonstrated knowledge of current trends in the discipline
Education and Experience: Preferred
- Significant experience as a practitioner in work related to your academic discipline
- Teaching non\-traditional students (e.g., working adults)
- Teaching with an online learning management system
- Application of rubrics to feedback and grading
Trident generally compensates its Adjunct Faculty on a per student basis rate that takes into consideration a variety of factors including, degree level taught, tenure with the University, and Committee Member or Chair Doctoral level assignment. The base rate for an Adjunct Faculty (non\-Committee/Chair Doctoral work) can range between $100\.00 and $300\.00 per student. The rate for an Adjunct Committee Member is $100\.00 per student per session and for an Adjunct Committee Chairs it is $500\.00 per student per session.
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: Base rate for an Adjunct (non\-Committee/Chair Doctoral work) can range between $100 to $300 per student
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. Mid-level AI roles across all categories have a median of $131,300.
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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