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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
Serves as the Staff technical engineer and visionary responsible for building the infrastructure, tooling, and frameworks that ensure our Artificial Intelligence and Machine Learning systems are safe, compliant, transparent, and trustworthy.
This is a high\-impact, cross\-functional role. You will bridge the gap between cutting\-edge AI engineering, legal compliance, and ethical safety standards. You will design and implement scalable systems to monitor, audit, and govern large language models (LLMs), predictive models, and autonomous agents across the entire enterprise product lifecycle.
You Will:
- Architecture \& Core Engineering: Design, build, and maintain enterprise\-grade AI governance platforms, including automated pipelines for model lineage, bias detection, drift monitoring, and compliance auditing.
- Agentic Guardrails \& Orchestration: Design and enforce deterministic guardrails for autonomous AI agents, ensuring agent reasoning loops cannot execute actions that bypass HIPAA controls or data boundaries.
- Standards \& Frameworks: Establish engineering standards, best practices, and internal processes for reproducible ML and responsible AI deployment.
- Technical Leadership: Mentoring mid\-level and senior engineers, driving technical roadmaps, and making critical architecture decisions regarding AI safety.
- Integration Platform Direction: Contribute to the technical direction of the integration platform by proposing and implementing improvements to architecture, tools, and technologies.
- Production Reliability: Identify and resolve performance bottlenecks, troubleshoot integration issues, and proactively improve the reliability of integrations in production environments.
- Healthcare Compliance \& Security: Ensure all integration work adheres to healthcare compliance and security standards, including HIPAA, HL7, and FHIR, while collaborating with security teams to protect sensitive data.
- Cross\-Functional Collaboration: Partner closely with Legal, Compliance, Security, Data Privacy, and AI Research teams to translate complex regulatory requirements into concrete, automated technical solutions. Vendor assessment and risk management along with secure LLM integrations.
Your Qualifications:
Required:
- Experience: 8\+ years of professional software engineering experience, with at least 3\+ years specifically focused on production ML systems, MLOps, or AI safety infrastructure.
- Architecture Decision\-Making: Proven track record of designing and implementing end\-to\-end solutions and making successful architectural decisions in complex, enterprise\-scale environments.
- Programming Mastery: High proficiency in Python and at least one systems language (e.g., Go, Java, C\+\+).
- AI/ML Expertise: Strong understanding of LLM architectures, transformer models, retrieval\-augmented generation (RAG), and traditional ML algorithms.
- Building/assessing enterprise\-grade AI proxy layers and tools to automatically detect, redact, or anonymize PHI before data reaches external LLM APIs
- Familiarity with LLM\-specific security vulnerabilities (e.g., OWASP Top 10 for LLMs)
- Adapting commercial AI models (like OpenAI or Anthropic) into healthcare workflows safely.
Preferred:
- Education: B.S. or M.S. in Computer Science, Data Science, or a related technical field (or equivalent practical experience).
What Success Looks Like:
- In 3 Months: Map out the existing AI/ML deployment pipeline, identify critical governance gaps, and deliver a technical roadmap for automated compliance auditing.
- In 6 Months: Launch the first version of our centralized AI Governance, enabling product teams to seamlessly integrate model tracking, bias testing, and safety guardrails into their existing CI/CD workflows.
- In 12 Months: Establish a comprehensive, real\-time AI risk\-monitoring dashboard that successfully scales to support all enterprise\-wide AI systems, ensuring 100% compliance with relevant regulatory standards.
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.
\#LI\-JB1 \#LI\-Hybrid
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 in the lower quartile for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At SCAN Health Plan, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($144K) sits 34% below the category median. Disclosed range: $106K to $182K.
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.
SCAN Health Plan AI Hiring
SCAN Health Plan has 3 open AI roles right now. They're hiring across AI Engineering Manager, AI Software Engineer, AI Architect. Positions span Long Beach, CA, US, Lakewood, CA, US. Compensation range: $182K - $254K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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