Interested in this AI/ML Engineer role at Oregon Health & Science University?
Apply Now →Skills & Technologies
About This Role
Department Overview:
Reporting to the Chief Financial Officer, the Analytics, Decision Support, and Applied AI leader has oversight and accountability for identifying and developing analytical or AI enabled solutions to complex financial and operational challenges within the Knight Cancer Institute. The Leader leverages Knight enterprise data operations managed by the Office of the Knight Chief Data Officer and overseen by the Knight Director of Data Strategy and Innovation and partners with them to shape the enterprise data roadmap, so it reflects finance and operational priorities.
In partnership with leadership, physicians, and operations, the leader brings decision support, automation and repeatability to solutions by leveraging enterprise applications. The leader brings domain expertise in clinical operations, revenue cycle, payor evaluation, clinical costing and margin analysis, general ledger, human resources, sponsored research, clinical trials, capacity and utilization planning, market analysis, physician productivity and general benchmarking.
The intersection between domain expertise and analytical and AI tools is where the leader can bring tremendous value to the Knight Cancer Institute. The leader will support business planning priorities by quantifying return on investment and portfolio prioritization.
The leader partners with Chief Technology and Data officer on data strategy and governance. Provides executive insight and direction on opportunities to apply AI in business processes to enhance efficiency and further the Knight’s mission.
The leader partners with Knight Chief Data officer and the Knight Director of Data Strategy and Innovation on data strategy and governance as a member of the Knight data governance committee. The leader adheres to data, metric and AI governance standards set by the Office of the Chief Data Officer. Identifies and champions opportunities to apply AI in finance and business processes to enhance efficiency and further the Knight’s mission
In partnership with the Chief Financial Officer (CFO), the role is also responsible for working collaboratively, and supporting, strategic and operational programs and processes across OHSU consistent with enterprise data and AI policies.
This role maintains the highest standards of integrity, collaboration, loyalty and enthusiasm, is self-confident and has the presence, knowledge base, and stature to gain the respect of peers, subordinates, physicians, staff, board members, executives, external stakeholder groups – and is effective in working within and across the enterprise.
Follows established KCI and department policies, procedures, objectives, performance improvement, attendance, safety, environmental, and infection control guidelines, including adherence to the workplace Code of Conduct. Practices a high level of integrity and honesty in maintaining confidentiality. Function/Duties of Position:
- Leverages analytic and AI tools to solve the most complex financial and operational and financial challenges facing Knight Cancer Institute.
- Maintains and refreshes the foundational elements of the long-range plan, including all assumptions impacting KCI operating and financial performance. Long-range plan distinctly outlines the oncology market, financial and operating impacts and commitments related to investments in facilities, equipment, programs, physicians.
- Deploys analytic and AI solutions that advance KCI strategy, optimize capacity utilization, bring efficiency to operations.
- Develops sources of truth related to operating efficiency, financial performance, clinical and research productivity.
- Defines business requirements and metric definitions for operating efficiency, financial performance, and clinical and research productivity.
- Quantifies return on investment and supports portfolio prioritization for business planning, capital and program investment decisions
Required Qualifications:
- Bachelor’s degree in Computer Science, Statistics/Mathematics, Data Science, Economics or related field
- Minimum of eight (8) years’ experience in healthcare financial and operational leadership roles. Experience in broad based delivery system preferred.
- Ability to translate business problems into analytical/AI solutions. Deep understanding of ML/AI methods and data architecture literacy. Expertise in Python, SQL, R.
Preferred Qualifications:
- Master’s degree in Business, Statistics, Data Science or a related field.
- Experience in academic medical center and stand alone cancer center settings, faculty practice plans, and research integration is highly desirable.
- Expertise in executive storytelling with data, AI and data governance, model risk and explainability.
Additional Details:
- Position requires some travel from site to site throughout Oregon.
- The incumbent must be able to perform all of the essential duties with or without accommodation.
Why apply to OHSU?: We are Oregon's only public academic health center. In addition to caring for patients, we lead groundbreaking research. We also train the next generation of health care professionals. As Portland's largest employer, we give you opportunities to learn and advance in a system of hospitals and clinics across Oregon and Southwest Washington. All are welcome. OHSU welcomes people of all ages, ethnicities, genders, national origins, religions and sexual orientations. We are striving to build an anti-racist, multicultural institution and encourage people with diverse backgrounds to apply. To request reasonable accommodation, contact [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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Oregon Health & Science 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 $211,950 based on 13,048 positions with disclosed compensation.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Oregon Health & Science University AI Hiring
Oregon Health & Science University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Portland, OR, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.