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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 411 Pay Range: $161,000.00 - $249,500.00Job Description
WGU’s Student Experience team builds the student portal that working adults use to navigate their education, monitor progress, connect with mentors, and determine what comes next. The team is now developing an AI assistant integrated into the portal and grounded in WGU’s systems to answer students’ questions about their individual degree journeys.
As a Staff Software Engineer, AI, you will be the technical center of this work. You will design and build the assistant’s production foundation, including retrieval, inference, guardrails, evaluation, observability, and a security model that isolates each student’s information. This is a hands-on product engineering role for someone who wants to set technical direction while continuing to design, code, test, and operate software.
You will help simplify WGU’s complex technology landscape from the student’s perspective while contributing to an ambitious effort to apply AI meaningfully within higher education. You will join a culture that encourages engineers to operate as inventors, frame undefined problems, and build solutions beyond the boundaries of individual backlog items.
What You’ll Do
- Design and build the chat foundation for the student portal assistant, including retrieval over WGU content and data, inference integration, conversation state, and the supporting service layer.
- Own the assistant’s security and privacy model, building authorization-aware retrieval and generation that protect student information and meet FERPA obligations.
- Build guardrails and governance capabilities, including input and output filtering, refusal and escalation paths, prompt-injection resistance, audit logging, and explainability controls.
- Establish evaluation, regression testing, and observability practices that measure groundedness, accuracy, quality, latency, and cost for nondeterministic AI outputs.
- Design, build, test, and operate full-stack portal features across services, APIs, data access, and user interfaces within an established Java and Angular environment.
- Integrate the assistant with systems of record, identity providers, and internal services, and participate in production support and service restoration.
- Mentor engineers through technical design, code review, and pairing while documenting decisions and modeling strong AI-assisted development practices.
What You’ll Bring
- Bachelor's Degree in Computer Science, Information Technology, or related field.
- 8+ years of professional software engineering experience building and operating production web applications and web services.
- Demonstrated technical ownership beyond individual feature delivery, including leading the design and build of a significant system or initiative and influencing technical decisions beyond your immediate work.
- 2+ years of hands-on experience building LLM-backed applications in production, including retrieval-augmented generation, inference integration, and evaluation of output quality.
- Full-stack production experience with a server-side language such as Java, Python, TypeScript/Node, C#, or Go, and a modern front-end framework such as Angular, React, or Vue.
- Experience integrating with databases and with third-party or enterprise systems in an existing infrastructure.
- The ability to design for security, privacy, scale, reliability, and cost while communicating technical decisions effectively to engineering, security, governance, and business partners. Equivalent relevant experience performing the essential functions of this job may substitute for education degree requirements. Generally, equivalent relevant experience is defined as one year of experience for one year of education and is at the discretion of the hiring manager.
Bonus Points
- Production experience with AWS Bedrock, Amazon SageMaker, or comparable managed model platforms, along with AWS, infrastructure as code, CI/CD, or cloud-native delivery.
- Experience with authorization-aware retrieval, multi-tenant data isolation, vector databases, hybrid search, LLM evaluation and observability, red-teaming, or prompt-injection testing.
- Experience with Angular, identity and access management, regulated data environments such as FERPA or HIPAA, established codebase modernization, or higher education technology.
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.
Work Location
This is a full-time, in-office position requiring five days per week in our Salt Lake City, Utah office, designed to foster the collaboration and connection that fuel our best work.
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.
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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 Software Engineer roles in our dataset (median: $193K across 172 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Western Governors University, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($205K) sits 7% below the category median. Disclosed range: $161K to $249K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Western Governors University AI Hiring
Western Governors University has 3 open AI roles right now. They're hiring across AI Software Engineer, AI Engineering Manager, AI/ML Engineer. Positions span Salt Lake City, UT, US, Raleigh, NC, US. Compensation range: $249K - $288K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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