Interested in this AI/ML Engineer role at Neumont College of Computer Science?
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About This Role
As a Neumont University instructor you will develop leadership and mentoring skills that will enhance your career. It can be a very rewarding experience as you see students start to understand complex subjects and gain confidence in their abilities.
Neumont is looking to fill multiple adjunct faculty positions to teach (in\-person) in the following area:
- Artificial Intelligence
Neumont University is looking for tech individuals with in\-industry experience to teach the following technologies:
Artificial Intelligence:
- Ability to teach AI fundamentals to complete beginners in clear, accessible terms
- Familiarity with low\-code AI tools, including Microsoft Azure ML Studio
AI Data Modeling:
- Proficiency in SQL/SQLite and relational database design (entity\-relationship modeling through normalization)
- Experience with dimensional modeling / data warehousing (star schemas, OLTP vs. OLAP)
- Understanding of how data modeling supports ML pipelines (feature stores, vector stores/embeddings, train/serve skew)
- Ability to teach query optimization, including indexes and reading query execution plans
- Strong working proficiency in Python, sufficient to review and debug student code live in class
Machine Learning Foundations:
- Comfort teaching and evaluating algorithmic complexity (Big\-O: time and space)
- Solid grounding in core ML concepts: supervised/unsupervised/reinforcement learning, overfitting, neural network fundamentals
Reinforcement Learning:
- Solid theoretical grounding in reinforcement learning (MDPs, Bellman equations, dynamic programming)
- Hands\-on experience with Monte Carlo methods, TD learning/SARSA, Q\-learning, policy gradients, and DQNs
- Working knowledge of NumPy and PyTorch
- Ability to teach on\-policy vs. off\-policy tradeoffs and deep\-RL stability concepts (replay buffers, target networks)
QUALIFICATIONS:
- Bachelor’s or higher degree in computer science or a related field AND 4 years of CS related experience (or 8 years of CS related experience without a CS degree)
- Teaching experience preferred, but not required
- Ability to work within the U.S. without company sponsorship
LOCATION: In\-person, on campus
TIME COMMITMENT:
- Courses begin October 5, 2026 and are 5 to 10 weeks long, depending on the course.
- We make classes work around full\-time work schedules as we offer AM and PM classes.
- Adjuncts may spend up to 10 hours a week outside of class doing grading and familiarizing themselves with the curriculum. This time commitment lessens once they get the hang of teaching.
ADVANTAGES TO BEING A NEUMONT FACULTY MEMBER:
- Improve the lives of students from across the nation through the power of education.
- Opportunity to give back through educating the next generation of tech experts.
- Experience the "light" turn on in your student's eyes as you teach and they experience true understanding.
- Be a part of a computer science institution that focuses on creating software engineers that can DO, not just theorize.
- Develop your teaching/mentoring skills.
Faculty at Neumont University are responsible for educating students in accordance with the Neumont teaching methodology, which focuses on active learning and engaging students in the learning environment. Faculty members are also responsible for grading and providing valuable feedback to students in a timely manner, mentoring students in groups or individually, evaluating curriculum, adapting coursework and materials as necessary to meet student learning needs, and other activities related to effective instruction.
RESPONSIBILITIES, INCLUDING BUT NOT LIMITED TO:
- Implement best practices in teaching and project\-based learning
- Submit all new teaching materials to Neumont vault upon completion of each course
- Work with supervisor to identify areas for personal development and course improvement
- Utilize feedback from mid\-quarter and end\-of\-quarter evaluations to improve teaching
- Identify innovative teaching methods to solve curricular problems
- Teach material defined in the course description and syllabus
- Maintain and meet the listed student learning goals
- Utilize the Neumont LMS to keep an updated syllabus, course materials, and grades
- Provide a safe learning environment for students
- Answer and deal respectfully with student complaints and problems
- Use effective assessments that measure student learning
- Provide timely and accurate feedback to students’ assignments, exams, projects, etc.
FAQ
I’ve never taught before, am I qualified to teach?
We hire industry professionals and help them learn how to be good teachers. Our project\-based curriculum means fewer lectures and more hands\-on practice. Instructors must have at least 4 years of industry experience and a bachelor’s degree in a tech related field. An additional 4 years of outstanding experience and contributions to the field may be substituted for a formal degree.
Do I have to develop the curriculum?
You will use curriculum that has been developed and refined by previous Neumont instructors. We expect that you will share your individual perspective and experiences with the students to supplement the formal curriculum.
Does Neumont offer online courses?
No. Our classes are in\-person as it makes for a better teaching/learning experience.
What is the process to get started?
All instructors will go through a formal application process which includes a short teaching demonstration. We will verify your work experience and educational credentials.
*NU is an equal opportunity employer and* *provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
Neumont’s Annual Security \& Fire Safety Report is available online at https://www.neumont.edu/campus\-safety under the Student Life section. This report is required by federal law to comply with the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and contains policy statements and crime statistics for the school. The policy statements address the school’s policies, procedures and programs concerning safety and security. You may also request a paper copy from the Vice President, Student Affairs.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Neumont College of Computer Science, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Neumont College of Computer Science AI Hiring
Neumont College of Computer Science has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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