Interested in this AI/ML Engineer role at Western Governors University?
Apply Now →Skills & Technologies
About This Role
If you’re passionate about building a better future for individuals, communities, and our country—and you’re committed to working hard to play your part in building that future—consider WGU as the next step in your career.
Driven by a mission to expand access to higher education through online, competency\-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student\-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families.
The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.
At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:
Grade: Technical 412
Pay Range: $161,000\.00 \- $249,500\.00Job Description
As a Staff AI Engineer, you will serve as a technical leader and force multiplier within the AI Engineering Enablement team. You will define how AI systems are architected, deployed, and scaled across the organization. Operating at the intersection of deep technical expertise and organizational leadership, you will shape platform strategy, influence roadmaps, and elevate engineering capabilities across multiple teams.### What You’ll Do
- Define and own AI engineering architecture standards, design patterns, and platform conventions for LLM\-based systems
- Lead complex, cross\-functional AI initiatives from inception through delivery, aligning engineering, product, data science, and research stakeholders
- Drive build\-vs\-buy and vendor evaluation decisions for AI frameworks, models, and infrastructure
- Design and scale internal AI platforms including shared tooling, reusable components, prompt libraries, and evaluation infrastructure
- Establish and mature LLMOps practices including governance, cost management, observability, and safe deployment standards
- Lead AI safety initiatives including red\-teaming, adversarial testing, and responsible AI policy development
- Mentor and develop Senior and II\-level engineers through coaching, design reviews, and technical leadership
### What You’ll Bring
- Expert\-level, production\-proven experience across the AI engineering stack including LLM APIs, agentic systems, RAG pipelines, evaluation frameworks, and LLMOps
- Demonstrated ability to define and drive architectural patterns and engineering standards at team or organizational scale
- Deep expertise in agentic system design including multi\-agent architectures, state management, and reliability engineering for non\-deterministic systems
- Strong platform engineering experience designing shared infrastructure, reusable tooling, and developer\-facing systems
- Advanced knowledge of LLM fine\-tuning, alignment techniques, and evaluation methodologies including safety and bias assessment
- Experience leading vendor evaluations and technical due diligence for AI frameworks and infrastructure
- Strong proficiency in Python and software engineering fundamentals with a focus on quality, testing, and reliability standards
- Bachelor’s Degree in Computer Science, Software Engineering, Data Science, Machine Learning, Math, or a related field. Master’s Degree strongly preferred.
- 7\+ years of experience in software engineering, data science, or machine learning
- 5\+ years of hands\-on experience building and deploying LLM\-based or AI systems in production at scale
- Demonstrated experience setting architectural direction across teams or organizations
- Experience leading complex AI projects across multiple teams or functional areas
- Proven mentorship of Senior and/or II\-level engineers
- Experience designing and operating shared AI platforms or internal AI infrastructure
- Experience owning LLMOps or MLOps practices including governance, rollout strategy, and production monitoring
- Experience with AWS cloud architectures including scalable inference, data pipelines, and cost optimization
- Hands\-on experience with fine\-tuning, PEFT, and model evaluation in production environments
### Bonus Points
- Master’s Degree or PhD in Computer Science, AI/ML, or a related field
- Experience with Databricks and related certifications
- AWS certifications such as AWS Certified Machine Learning – Specialty
- Experience with open\-source model ecosystems and self\-hosted inference infrastructure
- Experience in EdTech, personalized learning, or student\-facing AI platforms
- Published research, conference presentations, or open\-source contributions in AI/ML
- Experience with enterprise AI governance, compliance frameworks, or regulatory requirements
### Experience in Lieu of Education
Equivalent relevant experience performing the essential functions of this job may substitute for graduate education degree preferences or requirements.### What to Expect
At WGU, our mission drives everything we do, including how we hire. Our interview experience is designed to give qualified candidates the opportunity to show their best work through meaningful conversations and collaboration.
We thoughtfully review every application and invite forward the candidates whose experience and potential best align with the role and our mission.* Introductory call
- Hiring manager interview
- Technical interview
- Final panel interview
### Work Location
This is a full\-time, in\-office position requiring five days per week in our Raleigh, NC office, designed to foster the collaboration and connection that fuel our best work.### Visa Sponsorship
While we welcome applicants from all backgrounds, WGU is not able to provide visa sponsorship for this role.### Travel Requirement
This position requires occasional travel of up to 20%, including required attendance at designated company summits (typically one to two per year). Additional travel may include conferences, visits to company locations, and other business\-related events as needed. Additional travel may be assigned as needed to support business requirements.
\#LI\-AW2
Position \& Application Details
Full\-Time Regular Positions (classified as regular and working 40 standard weekly hours): This is a full\-time, regular position (classified for 40 standard weekly hours) that is eligible for bonuses; medical, dental, vision, telehealth and mental healthcare; health savings account and flexible spending account; basic and voluntary life insurance; disability coverage; accident, critical illness and hospital indemnity supplemental coverages; legal and identity theft coverage; retirement savings plan; wellbeing program; discounted WGU tuition; and flexible paid time off for rest and relaxation with no need for accrual, flexible paid sick time with no need for accrual, 11 paid holidays, and other paid leaves, including up to 12 weeks of parental leave.
How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.
Additional Information
Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It’s not all\-inclusive.
Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at [email protected].
Equal Employment Opportunity: All qualified applicants will receive consideration for employment without regard to any protected characteristic as required by law.
Salary Context
This $161K-$249K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Western Governors 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($205K) sits 6% below the category median. Disclosed range: $161K to $249K.
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.
Western Governors University AI Hiring
Western Governors University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US. Compensation range: $249K - $249K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.