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About This Role
Job Description
UW Information Technology has an outstanding opportunity for AI Security Engineer to join their team.
About this Opportunity
Reporting to Technology Manager, the AI Security Engineer will support the security, governance, and compliance of artificial intelligence (AI) initiatives at the university and its three campuses. The AI Security Engineer exists to secure the university's AI platforms, primarily Purple, built on Cloudforce nebulaONE and hosted in Azure AI Foundry, ensuring that AI services delivered to over 50,000 faculty, staff, and students across three campuses operate within a robust, compliant, and trustworthy security framework.
Key Responsibilities
\[30%] Security Engineering
- Implement and maintain security controls for AI platforms within Microsoft Azure, including network security groups, firewalls, encryption, key management, and secure landing zones aligned with the Azure Well\-Architected Framework (security pillar) and the Microsoft cloud security benchmark.
- Configure and manage identity and access management using Entra ID, RBAC, conditional access policies, and Zero Trust architecture principles across management\-group and subscription hierarchies.
\[20%] Security Automation \& Infrastructure\-as\-Code (DevSecOps).
- Implement and maintain infrastructure\-as\-code (IaC) security using Bicep and/or Terraform, including policy\-as\-code enforcement (Azure Policy, Sentinel policies) and IaC scanning in CI/CD pipelines.
- Embed security into CI/CD pipelines (DevSecOps) using GitHub Advanced Security or equivalent, including SAST, DAST, SCA, container image scanning, and auto\-remediation workflows.
- Develop security automation scripts and tools (Python, PowerShell, Bash) to streamline vulnerability scanning, configuration hardening, and compliance evidence collection.
\[20%] AI Application Security \& Red\-Teaming
- Support the security of AI application\-layer components specific to Purple and nebulaONE, including RAG data isolation, least\-privilege tool/function\-call authorization, agent action budgets and rate limits, output filtering, and secrets isolation.
- Participate in recurring red\-team exercises against AI platforms mapped to the OWASP LLM Top 10, the OWASP Top 10 for Agentic Applications, and MITRE ATLAS, documenting findings and supporting remediation.
- Assess and help mitigate AI\-specific security risks including prompt injection, jailbreak attacks, data leakage through model outputs, and adversarial attacks.
- Support implementation of guardrails for LLM and agent application patterns including RAG, tool/function calling, MCP (Model Context Protocol), and multi\-agent orchestration workflows.
- Apply AI security governance practices aligned with the NIST AI Risk Management Framework (AI RMF), the NIST Generative AI Profile, and ISO/IEC 42001\.
\[15%] Compliance Evidence \& Vendor Oversight
- Produce and maintain compliance evidence (not policy) for FERPA, HIPAA (where PHI is in scope, including areas outside UW Medicine), GLBA, NIST 800\-171/CMMC, and SOC 2 as it relates to AI platforms and cloud infrastructure.
- Support vendor security oversight of Cloudforce and Microsoft, including HECVAT completion/review, VPAT assessment, SOC 2 report analysis, data processing agreement reviews, and security questionnaire management.
\[10%] Incident Response \& Security Monitoring
- Monitor AI platform security posture using Azure Sentinel (SIEM), writing and tuning KQL queries for detection rules, alert triage, and threat hunting.
- Maintain and execute incident response playbooks specific to AI platforms, including data breaches, unauthorized access, prompt injection attacks, model compromise, and agent misuse scenarios.
- Triage and investigate security incidents, coordinate response activities with UWIT Office of Information Security, and contribute to post\-incident reports with root cause analysis.
\[5%] Paved\-Road Patterns, Documentation \& Continuous Improvement
\- Contribute to 'paved\-road' security patterns \- reusable, pre\-approved templates and configurations that make the secure path the easy path for developers and administrators.
- Develop and maintain security documentation, including architecture diagrams, runbooks, standard operating procedures, and incident response playbooks.
- Evaluate emerging security tools and technologies; make recommendations for adoption.
Required Qualifications
To be considered for this opportunity your application must demonstrate you meet both the minimum qualifications and additional qualifications listed below. Equivalent education and/or experience may substitute for minimum qualifications except when there are legal requirements, such as a license, certification, and/or registration.
Minimum Qualifications
- Bachelor's Degree in Cybersecurity, Information Security, Computer Science, Information Technology, or a related field, or equivalent combination of education and experience.
- 3\+ years of experience in cloud security engineering, DevSecOps, or infrastructure security with hands\-on cloud platform experience.
- Hands\-on experience with Azure security services such as: Defender for Cloud, Sentinel, Entra ID/RBAC, Azure Policy, Key Vault, and network security configurations. Candidates with equivalent depth in AWS or GCP who can demonstrate the ability to ramp on Azure are also encouraged to apply; Azure experience is strongly preferred.
- Understanding of Zero Trust architecture principles, identity governance, and conditional access.
- Experience with container or Kubernetes security concepts.
- Proficiency in Python, PowerShell, or Bash for security automation.
- Experience with SIEM tools (Azure Sentinel or equivalent) and incident response.
- Working knowledge of at least two compliance frameworks: FERPA, HIPAA, NIST 800\-53/800\-171, or SOC 2\.
- Strong written and verbal communication skills with the ability to explain security concepts to both technical
Applicants who do not meet these qualifications WILL NOT be forwarded to the Hiring Manager.
Preferred Qualifications
- Microsoft Certified: Cloud and AI Security Engineer Associate
- SC\-100 (Cybersecurity Architect Expert), SC\-200 (Security Operations Analyst).
- HashiCorp Terraform Associate certification.
- Experience with infrastructure\-as\-code (Bicep and/or Terraform), including IaC scanning and policy\-as\-code concepts.
- Experience embedding security into CI/CD pipelines: SAST, DAST, SCA, or container scanning.
- CISSP or CISM (note: CISSP requires 5 years experience, which may be aspirational for mid\-level candidates).
- Exposure to AI/ML application security risks: prompt injection defense, RAG data isolation, agent authorization, output filtering.
- Familiarity with OWASP LLM Top 10, OWASP Top 10 for Agentic Applications, or MITRE ATLAS.
- Knowledge of AI governance frameworks: NIST AI RMF, NIST Generative AI Profile, ISO/IEC 42001\.
- Experience with the Azure Well\-Architected Framework (security pillar) and Microsoft cloud security benchmark.
- Production multi\-tenant SaaS or AI\-platform operations experience.
- Azure cost\-management and FinOps awareness.
- Experience in higher education or public sector IT.
- Experience with HECVAT, VPAT, and vendor security assessment processes.
- Familiarity with penetration testing methodologies and tools.
- Knowledge of Washington My Health My Data Act, GLBA, GDPR.
- Experience with Agile/Scrum methodologies and tools (Jira, Azure Boards).
Working Conditions
This is a hybrid position with two days in office per week (one required team day on Wednesday, one flex day chosen by the employee). Work is conducted in an open office environment with daily interactions with team members, subject matter experts, and stakeholders at all levels of the organization.
While the general working hours are Monday through Friday, 8:00 AM to 5:00 PM, the AI Security Engineer will participate in an on\-call rotation for critical AI platform security incidents and may need to adjust hours to accommodate security events, business needs, and deadlines.
About the Team
University of Washington is at the forefront of leveraging cutting\-edge technologies to transform education, research and healthcare. UW Information Technology (UW\-IT) is the central IT organization for the University of Washington, collaborating with partners across the University community to advance teaching, learning, innovation and discovery. UW\-IT delivers critical IT services and support to all three campuses, UW medical centers and global research operations. Innovation and discovery are at the heart of what UW\-IT does and drive the work in advancing the University of Washington's role and mission.
This role is pivotal in ensuring that AI platforms and services operate within a robust security framework that aligns with institutional policies, federal regulations, and industry best practices. As a core member of the AI Platforms team, the AI Security Engineer will be responsible for developing and enforcing security standards, conducting risk assessments, managing compliance requirements, and collaborating across teams to embed security into every phase of the AI lifecycle.
Compensation, Benefits and Position Details
Pay Range Minimum:
$87,624\.00 annual
Pay Range Maximum:
$142,392\.00 annual
Other Compensation:
*
Benefits:
For information about benefits for this position, visit https://www.washington.edu/jobs/benefits\-for\-uw\-staff/
Shift:
First Shift (United States of America)
Temporary or Regular?
This is a regular position
FTE (Full\-Time Equivalent):
100\.00%
Union/Bargaining Unit:
Not Applicable
About the UW
Working at the University of Washington provides a unique opportunity to change lives – on our campuses, in our state and around the world.
UW employees bring their boundless energy, creative problem\-solving skills and dedication to building stronger minds and a healthier world. In return, they enjoy outstanding benefits, opportunities for professional growth and the chance to work in an environment known for its diversity, intellectual excitement, artistic pursuits and natural beauty.
Our Commitment
The University of Washington is committed to fostering an inclusive, respectful and welcoming community for all. As an equal opportunity employer, the University considers applicants for employment without regard to race, color, creed, religion, national origin, citizenship, sex, pregnancy, age, marital status, sexual orientation, gender identity or expression, genetic information, disability, or veteran status consistent with UW Executive Order No. 81 .
To request disability accommodation in the application process, contact the Disability Services Office at 206\-543\-6450 or [email protected] .
Applicants considered for this position will be required to disclose if they are the subject of any substantiated findings or current investigations related to sexual misconduct at their current employment and past employment. Disclosure is required under Washington state law .
Salary Context
This $87K-$142K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At University Of Washington, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($115K) sits 46% below the category median. Disclosed range: $87K to $142K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
University Of Washington AI Hiring
University Of Washington has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $142K - $142K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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