Interested in this AI/ML Engineer role at University of Alaska?
Apply Now →About This Role
- 533605
- Anchorage, Alaska, United States
- Hybrid
- On Campus
- Staff Full-time
- Grade 79
- UAA Office of Research
Are you passionate about research, innovation, and the transformative potential of artificial intelligence? Join the Sponsored Project Administrative Services (SPAS) team as a Junior Project Manager and AI Analyst and help advance impactful research and externally funded projects through effective project coordination, data-driven decision-making, and responsible application of AI technologies. This is a unique opportunity to contribute to high-impact research, support innovative solutions, and help shape the future of research administration. If you thrive in a collaborative environment, enjoy solving complex challenges, and are excited about leveraging emerging technologies to improve research outcomes, we encourage you to apply. Bring your research expertise, project management skills, and AI experience to the SPAS team and help drive research excellence and innovation across the University, Alaska and beyond!
As the SPAS Junior Project Manager & AI Analyst, you will support the planning, coordination, and execution of research and externally funded projects with a particular emphasis on artificial intelligence initiatives and research innovation. Working closely with Principal Investigators, faculty, researchers, and project teams, you will help define research questions, identify appropriate AI applications, develop project-specific workflows and prototypes, and evaluate the effectiveness of AI-assisted research tools. You will conduct literature reviews, gather and synthesize quantitative and qualitative information, assess research findings, develop reports and presentations, and assist in establishing protocols that support the responsible, effective, and compliant use of AI technologies in research environments. Through collaboration with faculty and research professionals, you will contribute to research products, evaluation activities, and project outcomes that advance institutional research goals.
You will coordinate assigned components of complex research and sponsored projects by maintaining schedules, work plans, milestone trackers, reporting calendars, and project documentation systems that support successful project execution. You will assist Principal Investigators, project managers, and research administrators in translating project objectives into organized tasks, deadlines, deliverables, and reporting requirements. In this role, you will facilitate communication among researchers, sponsors, stakeholders, committees, and external partners; coordinate meetings, workshops, and demonstrations; prepare agendas, meeting summaries, action items, and project records; and support the preparation of sponsor reports and project documentation. You will also develop and maintain practical templates, tracking tools, and procedures that improve project organization, reporting, accountability, and operational efficiency.
You will provide fiscal and administrative support for assigned research projects, including the university's AI-related initiatives. Responsibilities include maintaining budget tracking tools, monitoring expenditures, supporting account reconciliations, assisting with financial reporting, tracking procurements and project costs, and ensuring required fiscal documentation is complete and routed appropriately. You will help prepare budget revisions, funding modifications, expenditure forecasts, and fiscal status summaries while maintaining awareness of sponsor requirements, university procedures, and award terms. By working collaboratively with research administration, fiscal staff, faculty, and project teams, you will help ensure projects remain organized, compliant, financially responsible, and positioned to achieve successful outcomes.
To thrive in this role, the successful candidate should possess a strong blend of research administration expertise, project management experience, and advanced technological fluency, particularly in the application of artificial intelligence and large language models (LLMs) within research and administrative settings. They should possess demonstrated experience supporting complex research initiatives and externally funded projects while leveraging AI-enabled technologies to improve data analysis, information synthesis, workflow efficiency, reporting quality, and overall project outcomes. A thorough understanding of AI capabilities, limitations, responsible use practices, human oversight requirements, output validation, and data governance principles should be central to their professional background.
The ideal candidate should possess strong analytical and organizational skills, with experience coordinating multiple concurrent projects, managing schedules and deliverables, supporting budgets, and synthesizing complex quantitative and qualitative information into clear reports, presentations, and recommendations. They should possess the ability to evaluate emerging technologies, develop project-specific AI use cases, assess outputs against established criteria, and integrate AI tools into research and administrative processes while maintaining accuracy, compliance, and appropriate human review.
Success in this position should be supported by exceptional communication and collaboration abilities, enabling the candidate to work effectively with faculty, researchers, staff, government agencies, industry partners, and other stakeholders. The successful candidate should possess a demonstrated ability to develop innovative workflows, project tracking systems, and documentation practices that improve efficiency and accountability. Candidates with practical experience applying AI and LLM technologies to real-world research, project management, knowledge-management, and operational challenges, combined with strong research coordination and sponsored-project experience, should be particularly well positioned to excel in this dynamic and evolving environment.
Minimum Qualifications:
Bachelor’s degree and one year of research experience in a field relevant to the research (e.g. project management, public policy, math, business, data analysis, social science), or an equivalent combination of training and experience.
Position Details:
This position is located in Anchorage, Alaska, and flexible on-site/hybrid work arrangements may be considered in accordance with UA regulations. This is a term-funded, full-time, exempt staff position complete with both a competitive salary and full employee benefits package. UA provides a generous compensation package that includes retirement options, annual leave, 12 paid holidays per year, tuition waivers for employees and family members, and affordable medical, dental and vision care coverage. New hires will be placed on the UA Staff Salary Schedule, Grade 79, based on education and experience.
Application review will begin on November 02, 2026. Applications received by this date will receive priority consideration; however, the recruitment will remain open until filled.
The University of Alaska (UA) requires employees to work on-site based on operational and business needs. Hybrid and remote work arrangements may be considered only for positions specifically designated and advertised as eligible for hybrid or remote work. Any approved arrangement will be based on the position’s duties, operational needs, and the employee’s ability to effectively perform the work remotely.
- ️If you have any questions regarding this position, please contact Kelsie Kingston at [email protected].
- To be eligible for this position, applicants must be legally authorized to work in the United States without restriction. Applicants who now or may in the future require visa sponsorship to work in the United States are not eligible.
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This is a term-funded position reviewed annually for contract renewal at the University's discretion.
The University of Alaska (UA) is responsible for providing reasonable accommodations to individuals with disabilities throughout the applicant screening process. If you need assistance in completing this application or during any phase of the interview process, please contact the ADA team at [email protected].
The University of Alaska (www.alaska.edu) is an Equal Opportunity/Equal Access Employer and Educational Institution. The University is committed to a policy of non-discrimination (www.alaska.edu/nondiscrimination) against individuals on the basis of any legally protected status.
The successful applicant is required to complete a background check. Any offer of employment is contingent on the background check.
Your application for employment with the University of Alaska is subject to public disclosure under the Alaska Public Records Act.
All employees at the University of Alaska are expected to uphold the highest standards of ethics and professionalism when conducting or engaged in university business.
- Each university within the University of Alaska system publishes an Annual Security and Fire Safety Report which contains information regarding campus safety and security including topics such as: campus law enforcement authority; crime reporting policies; campus alerts (Timely Warnings and Emergency Notifications); fire safety policies and procedures; programs to prevent dating violence, domestic violence, sexual assault and stalking; the procedures the University will follow when one of these crimes is reported; and other matters of importance related to security on campus. The report also contains information about crime statistics for the three most recent calendar years concerning reported crimes that occurred on campus; in On-Campus Student Housing Facilities; in Noncampus buildings or property owned or controlled by the University or a recognized student organization; and on public property within, or immediately adjacent to and accessible from, the campus. The report also contains fire statistics for any fires occurring in an On-Campus Student Housing Facility during the three most recent calendar years.
Access to the reports is available at:
UAA: (Addresses Anchorage campus, Aviation Technology Complex, JBER - Elmendorf Extension, JBER - Richardson Extension, Kenai Peninsula College - Kachemak Bay campus, Kenai Peninsula College - Kenai River campus, Kodiak College, Kodiak High School Extension, Matanuska-Susitna College, Prince William Sound College, and Prince William Sound College - Cordova Extension) Online: https://www.uaa.alaska.edu/students/safety. Request a paper copy in person: UAA Police Department Office at Room 114 of Eugene Short Hall on the Anchorage campus / UAA Dean of Students Office at Room 122 of Rasmuson Hall on the Anchorage campus. Request a paper copy by mail: 907-786-1120 or [email protected] / 907-786-1214 or [email protected].
UAF: (Addresses Fairbanks Campus, Bristol Bay Campus, Chukchi Campus, Community and Technical College, Kasitsna Bay Campus, Seward Marine Center, Tok Campus, Kuskokwim Campus, and Northwest Campus) Online: https://www.uaf.edu/orca/files/ASFSR.pdf. Request a paper copy in person: UAF Office of Rights, Compliance and Accountability on the 3rd Floor of Constitution Hall. Request a paper copy by mail: 907-474-7300 or [email protected].
UAS: (Addresses the Juneau Auke Bay Campus, Juneau Technical Education Center, Sitka Campus, & Ketchikan Campus) Online: https://uas.alaska.edu/compliance/clery-act/annual-security-reports.html. Request a paper copy in person: Hendrickson Building, Suite 202 on the Juneau campus. Request a paper copy by mail: 907-796-6371 or emailing [email protected].
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At University of Alaska, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
University of Alaska AI Hiring
University of Alaska has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Anchorage, AK, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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