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About This Role
Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first. We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together.
The Principal Artificial Intelligence & Automation Engineer is a senior subject matter expert responsible for defining and executing the AI and automation strategy that powers Alignment Health’s Medicare Advantage business. This role will design, deliver, and scale AI-powered systems and intelligent automation that improve care quality, member experience, and operational performance. Partnering closely with DTS leadership, Clinical Operations, Finance, Claims, and Product, this role will identify the highest-value AI and automation opportunities, translate them into a cohesive roadmap, and ensure reliable, compliant delivery in a highly regulated environment. This role is pivotal in shaping how Alignment applies cutting-edge AI and automation to reduce administrative burden, drive payment accuracy, and unlock zero wasted potential in how work gets done across the organization.Job Duties/Responsibilities
Set AI & Automation Strategy Aligned to Medicare Advantage Priorities
- Define and maintain a multi-year AI and automation roadmap that supports strategic goals in Stars, risk adjustment, payment accuracy, prior authorization, clinical quality, and member experience.
- Partner with C-suite and senior leaders to identify and prioritize high-ROI use cases where AI and intelligent automation create durable competitive advantage.
Drive Intelligent Automation
- Lead the design and implementation of enterprise-grade automation solutions, including RPA, workflow orchestration, and process intelligence, across functions such as Claims, UM, Provider Operations, and Member Services.
- Establish standards and governance for automation development, documentation, and change management to ensure scalability and maintainability.
Establish Platforms, Tooling, and Engineering Best Practices
- Own the selection and adoption of AI/ML platforms, cloud services (AWS/Azure/GCP), and automation tools (e.g., UiPath, Power Automate), ensuring they integrate with DTS data platforms and security controls.
- Define and enforce engineering best practices, including CI/CD, MLOps, testing, observability, and documentation, that raise the bar for reliability and time-to-value across all AI and automation initiatives.
Partner Cross-Functionally to Deliver Measurable Business Impact
- Collaborate with Product, Data Engineering, Clinical Operations, Finance, Compliance, and Security to translate business requirements into technical solutions and track realized value (e.g., cycle time reduction, accuracy improvement, cost-to-serve).
- Regularly communicate AI and automation strategy, progress, and ROI to senior executives and governance bodies.
Embed Responsible AI and Regulatory Compliance
- Work with Legal, Compliance, and Privacy to ensure all AI and automation solutions meet CMS, HIPAA, and internal governance standards.
- Implement frameworks for bias detection, model explainability, auditability, and change control, recognizing that AI outputs directly influence member care decisions and financial outcomes.
Manage Budget, Vendors, and External Partnerships
- Own the operating budget for AI & Automation Engineering, including cloud spend, third-party platforms, and external partners.
- Evaluate and manage vendor relationships to ensure investments reflect labor market realities and maximize ROI.
Supervisory Responsibilities
- This role has no direct reports or people-management responsibilities. It provides technical leadership, mentoring, and cross-functional direction through expertise and influence rather than supervisory authority.
Job Requirements
EXPERIENCE
Required:
- 8–12 years of progressive experience in software engineering, data science, or AI/ML roles, with at least 3–5 years in a senior level position.
- Proven track record of delivering production-grade AI/ML systems and large-scale automation solutions in a regulated or enterprise environment.
- Deep expertise in machine learning, NLP, generative AI (LLMs, RAG pipelines), agentic frameworks, and intelligent process automation (RPA and orchestration).
- Experience managing budgets, vendor relationships, and technology platform decisions at a department or function level.
Preferred:
- Experience in healthcare, specifically Medicare Advantage, managed care, or payer environments, with fluency in use cases such as risk adjustment, prior authorization, claims processing, Stars ratings, and revenue cycle management.
- Demonstrated knowledge of healthcare data standards including HL7 FHIR, ICD-10/CPT, or DICOM as applied to AI systems.
- Track record of building and scaling an AI or automation engineering function from early stage to enterprise maturity.
- Published research, conference presentations, or demonstrated thought leadership in AI/ML or enterprise automation.
EDUCATION
Required:
- Bachelor's degree in Computer Science, Computer & Electrical Engineering, Mathematics, Data Science, or a related quantitative field.
- Equivalent combination of education and demonstrated experience will be considered.
Preferred:
- Master's degree or PhD in Computer Science, AI/ML, or a related quantitative discipline.
- Advanced or specialty cloud certification from AWS, Microsoft Azure, or Google Cloud (AI/ML, data, or architecture track).
- RPA platform certification at an advanced or architect level (UiPath, Automation Anywhere, or Microsoft Power Automate).
TRAINING
Required:
- Demonstrated senior-level proficiency with AI/ML engineering practices, cloud platforms, and enterprise automation through professional experience; formal training or equivalent self-directed mastery accepted.
- Working knowledge of cloud AI/ML governance and platform management (AWS, Azure, or GCP) at a leadership level; certification or equivalent experience accepted.
Preferred:
- Executive or senior-level certification or coursework in AI strategy, responsible AI governance, MLOps, or cloud AI platforms.
- Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate) or advanced MLOps tooling.
- Participation in AI/ML industry bodies, advisory boards, or standards organizations
SPECIALIZED SKILLS
- AI/ML Strategy & Technical Governance: Deep, hands-on understanding of the full AI/ML lifecycle, including model design, training, deployment, monitoring, and MLOps, with the ability to set organizational standards, make high-stakes platform decisions, and guide senior engineers through complex technical challenges.
- Intelligent Automation Leadership: Proven ability to define and govern enterprise automation strategy using RPA platforms (UiPath, Power Automate, Automation Anywhere) and orchestration tools (Airflow, Prefect). Experience establishing automation governance frameworks, ROI accountability, and cross-functional program leadership.
- Cloud AI Platform Ownership: Expert-level familiarity with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks. Experience owning platform selection, cost governance, and architecture standards at a department level.
- Healthcare Domain & Regulatory Expertise: Deep working knowledge of HIPAA, CMS regulations, and data governance in regulated healthcare environments.Advanced fluency in healthcare AI use cases including risk adjustment, payment integrity, clinical NLP, and member engagement.
- Executive Communication & Stakeholder Influence: Ability to translate complex AI and automation strategy into clear executive narratives.Experience presenting to boards, C-suite, and governance bodies.Demonstrated success influencing senior decision-making through data, business outcomes, and technical credibility.
- Responsible AI & Governance: Applied leadership experience with ethical AI frameworks including bias detection, model explainability (SHAP, LIME, or equivalent), auditability, and responsible AI program design.Ability to embed governance standards organization-wide in a high-stakes healthcare context.
ESSENTIAL PHYSICAL FUNCTIONS
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- While performing the duties of this job, the employee is regularly required to talk or hear. The employee regularly is required to stand, walk, sit, use hand to finger, handle or feel objects, tools, or controls; and reach with hands and arms.
- The employee frequently lifts and/or moves up to 10 pounds. Specific vision abilities required by this job include close vision and the ability to adjust focus.
Pay Range: $172,364.00 - $258,547.00 Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc.
Alignment Health is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, protected veteran status, gender identity, or sexual orientation.
- DISCLAIMER: Please beware of recruitment phishing scams affecting Alignment Health and other employers where individuals receive fraudulent employment-related offers in exchange for money or other sensitive personal information. Please be advised that Alignment Health and its subsidiaries will never ask you for a credit card, send you a check, or ask you for any type of payment as part of consideration for employment with our company. If you feel that you have been the victim of a scam such as this, please report the incident to the Federal Trade Commission at https://reportfraud.ftc.gov/#/. If you would like to verify the legitimacy of an email sent by or on behalf of Alignment Health’s talent acquisition team, please email [email protected].
Salary Context
This $172K-$258K range is above the 75th percentile 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 Alignment Health, 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. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $172K to $258K.
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.
Alignment Health AI Hiring
Alignment Health has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Orange, CA, US. Compensation range: $195K - $258K.
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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