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About This Role
Founded in 1977 as the Senior Care Action Network, SCAN began with a simple but radical idea: that older adults deserve to stay healthy and independent. That belief was championed by a group of community activists we still honor today as the “12 Angry Seniors.” Their mission continues to guide everything we do.
Today, SCAN is a nonprofit health organization serving more than 500,000 people across Arizona, California, Nevada, New Mexico, Texas, and Washington, with over $8 billion in annual revenue. With nearly five decades of experience, we have built a distinctive, values-driven platform dedicated to improving care for older adults.
Our work spans Medicare Advantage, fully integrated care models, primary care, care for the most medically and socially complex populations, and next-generation care delivery models. Across all of this, we are united by a shared commitment: combining compassion with discipline, innovation with stewardship, and growth with integrity.
At SCAN, we believe scale should strengthen—not dilute—our mission. We are building the future of care for older adults, grounded in purpose, accountability, and respect for the people and communities we serve.
The Job
As an AI Product Owner, you will serve as the strategic driver behind SCAN’s agentic AI platform, translating vision into actionable outcomes. You’ll own the requirements, validation strategy, and delivery of value, ensuring that robust, scalable, and innovative AI solutions meet evolving business needs. Your expertise will be pivotal in identifying organizational patterns and transforming them into proposals for new AI functionality, positioning the platform at the forefront of enterprise innovation.
You’ll collaborate with cross-functional teams—including engineering, product, business, and compliance—to shape and deliver impactful AI features. By fostering strong partnerships and sharing technical knowledge, you’ll ensure solutions are practical, scalable, and aligned with business goals.
Essential Job Functions:
Requirements & Feature Ownership
- Gather, refine, and prioritize technical and business requirements for platform features and agents.
- Translate business needs and organizational patterns into clear, actionable specifications for engineering teams.
- Make trade-off decisions regarding implementation (e.g. AI vs. non-AI) and maintain a prioritized feature backlog.
Platform Strategy & Roadmapping
- Use market headwind/tailwind to scope and envision AI platform features, expanding from workflows, scheduling, triggers, and integration points.
- Strategize and roadmap development of APIs, Model Context Protocol (MCP), and knowledge/data management systems.
- Ensure seamless integration with internal and external data sources and services.
Business Alignment & Pattern Recognition
- Analyze organizational processes and identify recurring patterns that can be addressed with AI functionality.
- Propose and design new features that directly address business needs and drive measurable impact.
- Collaborate with cross-functional teams to ensure solutions are practical, scalable, and compliant.
Test Strategy & Quality Assurance
- Define acceptance criteria and test strategies for platform features and agents.
- Ensure rigorous validation of AI functionality and alignment with business goals.
- Define KPIs and oversee test coverage, reliability, performance metrics, and evals.
Documentation & Knowledge Sharing
- Maintain clear, up-to-date documentation for requirements, test strategies, workflows, APIs, and agents.
- Share technical knowledge and best practices with engineering and business teams.
- We seek Rebels who are curious about AI and its power to transform how we operate and serve our members.
- Actively support the achievement of SCAN’s Vision and Goals.
- Other duties as assigned.
Your Qualifications
- Bachelor's Degree or equivalent experience Computer science, Engineering, or a related field
- Demonstrates deep technical proficiency in AI platform architecture, agentic orchestration, workflow automation, and context engineering.
- Collaborates effectively with cross-functional teams—including engineering, product, business, and compliance—to deliver robust, scalable AI solutions.
- Communicates complex technical concepts clearly to both technical and non-technical stakeholders.
- Proactively identifies organizational patterns and translates them into actionable AI feature proposals.
- Exhibits strong stakeholder management and change leadership skills, driving alignment and adoption across the organization.
- Develops and executes comprehensive test strategies for complex AI systems, ensuring quality and reliability.
- Maintains up-to-date documentation and shares best practices to foster a culture of continuous learning and improvement.
- Demonstrates familiarity with vectors, embeddings, knowledge representation, and knowledge graph or graph RAG.
- Shows proficiency with Azure AI Studio, Azure ML, Cognitive Services, Azure OpenAI, API Management Gateway, MCP Gateway, AI Search Service, Snowflake, and Databricks.
Preferred Certifications or Licenses:
- SAFe Agile Certification or equivalent agile product ownership certification.
- Relevant certifications in Azure AI (e.g., Azure AI Engineer Associate, Azure Data Scientist Associate) or other cloud AI platforms.
- Certifications in data engineering, machine learning, or AI solution architecture are a plus.
Experience Preferred:
- Experience in AI platform architecture, agentic orchestration, workflow automation, or context engineering.
- Hands-on experience with Azure AI Studio, Azure ML, Cognitive Services, Azure OpenAI, API Management Gateway, MCP Gateway, and AI Search Service.
- Experience with Snowflake, Databricks, or similar data platforms.
- Background in healthcare (health plan/payer environment preferred), regulated industries, or enterprise-scale AI deployments.
- Proven ability to translate business needs into technical requirements and deliver outcomes through cross-functional teams.
- Strong stakeholder management, communication, and change leadership skills.
- Demonstrated ability to envision comprehensive test strategies for complex AI systems.
What's in it for you?
- Base Salary Range: $106,200 to $182,983 annually
- An annual employee bonus program
- Robust Wellness Program
- Generous paid-time-off (PTO)
- 11 paid holidays per year, 1 floating holiday, birthday off, and 2 volunteer days
- Excellent 401(k) Retirement Saving Plan with employer match
- Robust employee recognition program
- Tuition reimbursement
- An opportunity to become part of a team that makes a difference to our members and our community every day!
We're always looking for talented people to join our team! Qualified applicants are encouraged to apply now!
At SCAN we believe that it is our business to improve the state of our world. Each of us has a responsibility to drive Equality in our communities and workplaces. We are committed to creating a workforce that reflects our community through inclusive programs and initiatives such as equal pay, employee resource groups, inclusive benefits, and more.
SCAN is proud to be an Equal Employment Opportunity and Affirmative Action workplace. Individuals seeking employment will receive consideration for employment without regard to race, color, national origin, religion, age, sex (including pregnancy, childbirth or related medical conditions), sexual orientation, gender perception or identity, age, marital status, disability, protected veteran status or any other status protected by law. A background check is required.
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Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c)
Salary Context
This $106K-$182K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At SCAN Health Plan, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($144K) sits 32% below the category median. Disclosed range: $106K to $182K.
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.
SCAN Health Plan AI Hiring
SCAN Health Plan has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Long Beach, CA, US. Compensation range: $182K - $182K.
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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