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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 Senior AI & Automation Engineer is a senior individual contributor and technical leader on the Data & Technology Solutions team, responsible for architecting, building, and scaling intelligent AI systems and enterprise automation solutions that directly improve care quality and operational performance across our Medicare Advantage business. You will serve as a go-to technical expert for AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams — driving end-to-end delivery of production AI systems that span claims processing, risk adjustment, prior authorization, revenue integrity, and member engagement. This role carries a higher degree of independent ownership and technical authority than the mid-level equivalent, with expectations to lead complex, ambiguous initiatives from concept through production and to actively elevate the engineering practices of the broader team.* Lead the build of distributed, cloud-native systems for enterprise workloads. Own the design and build of services, APIs, and microservices that run reliably at scale in a regulated environment. Set the standard for distributed systems practice on the team: fault tolerance, retries and idempotency, queuing and eventing, caching, and horizontal scaling.
- Lead hybrid infrastructure work bridging cloud and non-cloud systems. Own the build of integrations with non-API data sources, including flat files, legacy databases, EDI feeds, mainframes, and streaming data, as well as multimodal data such as documents, images, and audio. Set integration patterns the rest of the team builds against.
- Own CI/CD, containerization, and deployment standards. Drive adoption of Docker, Kubernetes, and CI/CD best practices across the team. Own infrastructure-as-code standards for the systems the team builds.
- Own data and automation pipeline reliability. Build and govern ETL and orchestration pipelines at scale. Own automation strategy using RPA platforms and orchestration tools, including ROI governance and engineering standards for fault-tolerant workflow design.
- Apply AI/ML to production systems and own inferencing performance (applied, not research). Own the build of inferencing pipelines (batch, real-time, streaming) that serve models handed off from AI Sciences, for workloads such as claims processing, risk adjustment, and member communication. Partner directly with AI Scientists to take models from research into low-latency, high-throughput production systems, owning the benchmarking and tuning that gets them there. This is applied integration, serving, and performance ownership, not model research or training. Own the technical approach for integrating LLMs into workflows, including prompt engineering, RAG, and multi-agent orchestration frameworks (LangChain, LangGraph, AutoGen).
- Lead the Databricks build-out supporting AI Sciences. Own productionizing models and pipelines built on Databricks, including Delta Lake, MLflow, and Unity Catalog. Own the CI/CD, orchestration, access controls, and cost tuning that let Databricks-based work move reliably into production, and set standards for how the team uses the platform.
- Define alerting, testing, and monitoring frameworks that surface degradation before it affects member outcomes, and lead post-incident reviews.
- Mentor engineers and shape engineering culture. Provide senior technical mentorship through code reviews, design critiques, and paired problem-solving. Contribute to hiring, technical interviews, and team-wide engineering standards, including responsible AI practices such as bias detection, explainability, and governance.
Supervisory Responsibilities
Senior individual contributor role with no formal direct reports. Expected to function as an informal technical lead: mentoring, conducting code reviews, participating in hiring panels, and guiding the technical direction of junior and mid-level engineers on project teams.
Job Requirements
Required
- 5-8 years of professional software engineering experience with a demonstrated focus on distributed systems, cloud infrastructure, or data platforms
- Proven track record independently owning and delivering complex, production-grade distributed systems from concept through deployment and operations
- Experience building and scaling hybrid infrastructure connecting cloud-native and non-API/legacy systems
- Demonstrated experience serving and integrating AI/ML models or LLMs in production, including performance tuning (not model training/research)
- Experience in a regulated industry (healthcare, insurance, or financial services) with deep working knowledge of compliance and security requirements
- Experience building and scaling automation solutions using RPA platforms and workflow orchestration tools, including ROI governance
Preferred:
- Experience with the Databricks platform (Delta Lake, MLflow, Unity Catalog) at a senior or platform-owner level
- Experience applying AI/ML to complex healthcare data (claims, revenue cycle, prior authorization, medical coding, clinical text)
- Familiarity with HL7 FHIR, ICD-10/CPT, or DICOM
- Experience in a Medicare Advantage, managed care, or payer environment at a senior engineering level
- Prior experience as an informal technical lead, principal engineer, or engineering lead
Education:
- Required: Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or related quantitative field, or equivalent progressive senior-level experience
- Preferred: Master's degree in Computer Science or related quantitative discipline
License
- Required: None at this time
- Preferred: Advanced or specialty cloud certification (AWS, Azure, or GCP); RPA platform certification
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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