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About This Role
Initial Posting Date:
06/11/2026Application Deadline:
06/25/2026Agency:
Department of Administrative ServicesSalary Range:
$7,537 \- $11,098Position Type:
EmployeePosition Title:
Artificial Intelligence Policy and Safety Advisor (Operations \& Policy Analyst 4\)Job Description:
Now Hiring, Apply Today! Artificial Intelligence Policy and Safety Advisor (Operations \& Policy Analyst 4\)
Enterprise Information Services (EIS) is a state government\-wide information technology (IT) program led by Oregon’s Chief Information Officer (CIO). EIS provides statewide oversight for information technology management, planning, policy, and governance. Within EIS, the Artificial Intelligence (AI) Office, led by the Chief AI Officer, is charged with crafting the strategic vision for the State of Oregon relating to artificial intelligence.
What You’ll Do
As the AI Policy and Safety Advisor you will provide policy advice, research, communications, planning, and operational support to the Chief AI Officer to advance Oregon’s AI program. You will serve as a lead advisor to the Chief AI Officer in establishing practical and responsible frameworks for AI governance for state agencies, evaluating enterprise AI policy changes, developing communication and training programs, and collaborating with executive leaders on governance, risk management, and AI adoption strategies. The AI Advisor applies strategic judgment to develop and implement policies that support the enterprise\-wide adoption of responsible AI practices. In short, the AI Policy and Safety Advisor brings both an entrepreneurial mindset intent on supporting AI enablement at the state, and a healthy respect for safety and governance to make AI enablement safe and responsible. This position is the first of its kind in Oregon State Government and requires excellent judgment, interpersonal skills, and communication abilities to develop and implement policies that direct the enterprise\-wide adoption of responsible AI practices.
A full example of the position duties, details, and working conditions is located here.
This Is What You Need to Qualify
- A Bachelor's Degree in Business or Public Administration, Behavioral or Social Sciences, Finance, Political Science or any degree demonstrating the capacity for the knowledge and skills; and five years’ professional\-level evaluative, analytical and planning work\*.
- Any combination of experience and education equivalent to eight years of experience\* that typically supports the knowledge and skills for the classification.
- Qualifying experience which may include policy research, development and communication; risk management activities; advisory committee administration; standards development.
Underfill Opportunity
The goal of this recruitment is to fill this position as an Operations and Policy Analyst 4\. However, if the successful applicant does not meet the minimum qualifications for the Operations and Policy Analyst 4 classification but does meet the minimum qualifications for the Operations and Policy Analyst 3 classification, the applicant will be appointed to the classification of Operations and Policy Analyst 3 SR30, $6,834\.00\- $10,076\.00\. The duration and conditions surrounding the assignment and supervision of work are determined at the time of appointment.
The ideal candidate will possess the following desired skills and attributes:
- Demonstrated executive\-level expertise in policy development, evaluation, and communication on complex, technical topics applied to large enterprises.
- Excellent communication skills, both written and verbal. Proven ability to effectively and accurately explain complex, technical topics in a manner that is understandable to both technical and non\-technical audiences, including executive\-level leaders, through written and visual means.
- Experience using several of the major artificial intelligence platforms (Copilot, Claude, Gemini, ChatGPT), including prompt design and refinement and project scoping and development.
- Working knowledge of the NIST AI Risk management framework and the State of Oregon Enterprise AI Policy.
- Demonstrated ability to build a new program or function from the ground up, operating effectively amid an evolving mandate, shifting needs, and the absence of an established playbook or predecessor.
- Ability to support management team decisions and help drive consistent execution across the organization.
- Experience leading groups with diverse backgrounds and competing interests to consensus and actionable plans.
- Demonstrated ability to quickly learn new software tools. Experience with Microsoft Power Platform and the M365 Suite is desirable.
- Experience evaluating technology vendors and contracts for risk, including security, data handling, and contractual terms. AI\-specific familiarity a plus.
- Familiarity with IT operations and project/service delivery in a large organization, enough to ensure policy and governance recommendations are practical and implementable. Knowledge of a recognized framework such as ITIL or equivalent is a plus.
Benefits of Joining Our Team
The EIS team is built on collaboration and support. We work together to ensure our customer agencies receive the highest quality of service. We take pride in our work and look for ways to innovate. EIS is committed to hiring highly skilled, diverse and dedicated employees who will bring a unique skill set to the team.
Our amazing benefits include:
- Comprehensive Health Coverage: Low\-cost medical, vision, and dental plans for you and your family. Additional benefits include life insurance, short\- and long\-term disability, deferred compensation savings plans, and flexible spending accounts for health and childcare. Optional benefits including life insurance, disability, FSA, and more
- Generous Paid Time Off: 11 holidays, 3 personal business days, monthly sick leave and vacation leave that increases with years of service.
- Career Development: Opportunities for professional growth and advancement.
- Get There \- Oregon’s easy\-to\-use carpool matching tool and trip planner.
- Public Service Loan Forgiveness: You may qualify for the PSLF program.
- Hybrid Work Opportunity: This position supports a hybrid work schedule. You can expect to work in the office 1\-2 days per week, with work arrangements periodically reviewed to ensure business needs are met.
How to Apply
- Click on the "Apply" link above to complete your online application and submit by the posted closing date and time. For step\-by\-step instructions click apply to work for the state or current state employee.
- Required Documents: cover letter and resume
- The work experience and/or education section of your application must clearly demonstrate how you meet all the minimum qualifications and desired skills and attributes listed above. Failing to attach required documents may result in disqualification of your application.
- The State of Oregon does not request or require your age, date of birth, attendance or graduation dates from an educational institution during the application process.
- Be sure to check Workday and your email for additional tasks and updates. After hitting submit there may be additional required tasks for you to complete prior to the announcement closing. Please save a copy of this job announcement for reference, as it is not available for you to view after the announcement deadline.
- Eligible veterans and Oregon National Guard servicemembers (current and former) who meet the qualifications of the position will be awarded candidate preference. For more information, please visit Veterans Resources. Be sure to submit your documentation prior to the close date of this posting in order to have the preference considered.
Additional Details
- This announcement is for one, full\-time, permanent, Management Service Non\-Supervisory, Artificial Intelligence Policy and Safety Advisor (Operations \& Policy Analyst 4\) position and may be used to fill future vacancies.
- The salary listed is the non\-PERS qualifying salary range. If the successful candidate is PERS qualifying, the salary range will reflect an additional 6\.95%.Review the Classification and Compensation page for more details on the classification, or you may visit our website for information on the job offer process following pay equity.
- Applicants must be authorized to work in the United States. Applicants who require VISA sponsorship will not be considered at this time.
- Employee will be required to possess and maintain a valid driver’s license issued by the state where the employee resides.
- Finalists will be subject to a computerized criminal history check. Adverse background data may be grounds for immediate disqualification.
- If you need an application in an alternate format in order to complete the process or for accommodation requests under the Americans with Disabilities Act (ADA), you may contact the Recruiter, Nancy Karnas at: [email protected] \| 971\-719\-3083
Helpful Links \& Resources
How to Set Job Alerts \| Workday Applicant FAQ \| What You Need to Know to Get the Job
Oregon Job Opportunities Webpage \| Classification and Compensation \| Pay Equity
*The Department of Administrative Services is an Equal Opportunity, Affirmative Action Employer Committed to Workforce Diversity. At the Department of Administrative Services, we embody the value of hiring a workforce representative of the communities we serve, understanding that a diverse workforce revitalizes our state. We value diversity and foster a positive and welcoming environment where all employees can thrive.*
Salary Context
This $82K-$133K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Department of Administrative Services, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($107K) sits 41% below the category median. Disclosed range: $82K to $133K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Department of Administrative Services AI Hiring
Department of Administrative Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salem, OR, US. Compensation range: $133K - $133K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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