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About This Role
Included Health is seeking a hands‑on Staff AI Solutions Engineer to be part of our IT Solutions team. The right candidate will be passionate about the advancements in AI and will have experience in owning deployment/hosting decisions and implementation.
The engineer in this role will design, build, and operate internal automations, AI agents, and secure integrations that increase corporate teams productivity while meeting healthcare security and compliance requirements. This role blends software engineering, systems integration, architecture, and practical LLM expertise to take POCs to production.
The engineer in this role will partner closely with and build internal solutions for Cybersecurity, Compliance, Finance, HR, Product, Engineering, Operations, Clinical teams, and business stakeholders to deliver AI tooling with metrics\-backed value across the enterprise.
This role will report to the Director, Digital Workplace
### Responsibilities:
- Design, build, deploy, and maintain production LLM‑based solutions and agent workflows.
- Own the technical strategy and reference architecture for enterprise AI solutions across multiple teams and business functions.
- Lead high\-complexity, cross\-functional AI initiatives from ambiguous problem definition through production adoption and measurable business outcomes.
- Define and evolve reusable platform capabilities, implementation standards, and governance patterns that enable safe, scalable AI adoption beyond a single team.
- Review citizen developer AI agents / solutions to provide recommendations for optimization, ensure compliance with guidelines and measure value.
- Influence roadmap and investment decisions across the company through technical leadership and business\-value analysis.
- Drive technical debates, align stakeholders on tradeoffs, and unblock multi\-team execution for strategically important AI initiatives.
- Implement, review, and validate code produced by models; write production‑quality code and run code reviews to ensure correctness and security.
- Build robust integrations and connectors (MCP, REST/GraphQL APIs, webhooks, SDKs, CLIs) between AI tooling and enterprise SaaS (e.g., Okta, Google Workspace, Slack, Jira, Confluence, Jamf).
- Own end‑to‑end deployment and lifecycle for AI services: CI/CD pipelines, Infrastructure as Code modules (Terraform), cloud deployment (GCP/AWS), monitoring, and incident/runbook playbooks.
- Establish and operate model evaluation, monitoring, and governance: accuracy and safety metrics, hallucination detection, drift monitoring, telemetry, alerting, and human‑in‑the‑loop controls.
- Lead vendor evaluations and POCs across commercial and open‑source LLM/agent platforms; produce comparative performance, risk, and TCO recommendations to inform adoption.
- Partner with Cybersecurity and Compliance to design PHI‑safe data handling patterns (sanitization, tokenization, least‑privilege access, audit logging) and ensure AI solutions align with relevant controls and policies.
- Create and maintain architecture diagrams, API documentation, runbooks, support documentation, and onboarding materials so solutions are maintainable and auditable.
- Mentor engineers and influence architectural standards for AI/LLM adoption across Digital Workplace; contribute reusable libraries and IaC modules to accelerate future builds.
- Drive automation of operational tasks (provisioning, onboarding, common workflows) via agents and workflow tooling to reduce manual processes.
- Partner with Technology Services leadership to implement AI spend management tools and value tracking.
### Qualifications:
- Education \& Experience:
+ Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
+ 8\+ years professional software engineering / systems integration experience.
- Technical Skills:
+ Practical experience owning the complete lifecycle of LLMs and agent
+ Experience evaluating model performance, mitigating hallucinations and bias, and implementing human‑in‑the‑loop controls.
+ Ability to define reference architectures and reusable patterns for AI services used across multiple teams.
+ Experience making architectural tradeoffs across reliability, latency, cost, security, and maintainability in production systems.
+ Experience establishing engineering standards, guardrails, and paved\-road patterns for AI development and deployment.
+ Experience optimizing model/runtime cost, usage controls, and value measurement.
+ Strong coding experience in Python and/or TypeScript/JavaScript with production software engineering discipline (code reviews, testing, CI/CD).
+ Experience designing and building API integrations (REST/GraphQL), webhooks, and custom connectors to SaaS applications.
+ Experience with Infrastructure as Code (Terraform) and deploying services to cloud platforms (GCP/AWS).
+ Familiarity with CI/CD tooling and observability best practices (metrics, logs, tracing).
+ Working knowledge of security best practices for data‑sensitive systems and experience collaborating with Security \& Compliance teams. Experience in healthcare or other regulated environments is strongly preferred.
- Soft Skills:
+ Strong communicator and collaborator who can translate ambiguous business needs into technical designs and influence cross‑functional stakeholders.
+ Bias for action: ability to move quickly from POC to production while keeping operational rigor with change management.
+ Detail oriented with a security‑first mindset.
+ Proven ability to influence technical direction and align cross\-functional stakeholders without direct authority.
+ Skilled at leading technical debates, resolving conflict, and driving decisions in ambiguous, high\-stakes environments.
+ Strong executive communication skills, with the ability to translate complex technical concepts into clear business decisions and risk tradeoffs.
Bonus Points:
- Prior experience working in high\-growth, pre\-IPO companies and/or industry experience in Technology or Healthcare preferred
- Affinity for bad Jokes
### Physical/Cognitive Requirements:
Physical Requirements:* Stamina, strength, mobility, manual dexterity, vision, hearing, environmental factors.
- Ability to effectively communicate and engage with others, with accommodations available for visual or auditory impairments
- Ability to handle physical tasks related to the job, with accommodations provided as needed.
- Ability to respond effectively to changing situations, with accommodations available for varying response times.
- Prompt and regular attendance at assigned work location.
- Ability to remain seated in a stationary position for prolonged periods.
- Requires eye\-hand coordination and manual dexterity sufficient to operate keyboard, computer and other office\-related equipment.
- No heavy lifting is expected, though occasional exertion of about 20 lbs. of force (e.g., lifting a computer / laptop) may be required.
Cognitive Requirements:
- Problem\-solving, decision\-making, attention to detail, critical thinking, communication skills, stress tolerance.
- Facilitate communication and collaboration with clients and stakeholders.
- Adaptability to different work environments, with support provided for managing workload and pace
- Ability to maintain focus and attention on tasks, with accommodations available for individuals with attention\-related conditions.
- Ability to interact with leadership, employees, and members in an appropriate manner.
The base salary range for this full\-time position is $159,780\.00 – $238,080\.00 per year in the United States. This posted range reflects the portion of our internal salary band that is currently funded for new hires in this role across our standard labor markets (Zones A–D).
For context, these markets include Zone A (e.g., Phoenix AZ, San Antonio TX, Columbus OH, Charlotte NC), Zone B (e.g., Chicago IL, Denver CO, San Diego CA, Houston TX), Zone C (e.g., Los Angeles CA, Seattle WA, Washington, D.C., Boston MA), and Zone D markets (e.g., San Francisco Bay Area CA, New York City NY, San Jose CA) for this role. Within this range, individual pay is determined by work location, skills, experience, and internal equity. We use structured salary bands and geographic zones based on cost of labor to keep pay fair and consistent.
Benefits \& Perks:
In addition to receiving a great compensation package, the compensation package may include, depending on the role, the following and more:
- Remote\-first culture
- 401(k) savings plan through Fidelity
- Comprehensive medical, vision, and dental coverage through multiple medical plan options (including disability insurance)
- Paid Time Off ("PTO") and Discretionary Time Off (“DTO")
- 12 weeks of 100% Paid Parental leave
- Family Building \& Compassionate Leave: Fertility coverage, $25,000 for surrogacy/adoption, and paid leave for failed treatments, adoption or pregnancies.
- Work\-From\-Home reimbursement to support team collaboration home office work
Your recruiter will share more about the salary range and benefits package for your role during the hiring process.
About Included Health
Included Health is a new kind of healthcare company, delivering integrated virtual care and navigation. We’re on a mission to raise the standard of healthcare for everyone. We break down barriers to provide high\-quality care for every person in every community — no matter where they are in their health journey or what type of care they need, from acute to chronic, behavioral to physical. We offer our members care guidance, advocacy, and access to personalized virtual and in\-person care for everyday and urgent care, primary care, behavioral health, and specialty care. It’s all included. Learn more atincludedhealth.com.\-
Included Health is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, state, or local law. Included Health considers all qualified applicants with arrest or conviction records in accordance with the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance, and California law.
Included Health uses AI\-assisted tools at select stages of the hiring process to enhance efficiency, consistency, and communication. AI does not make hiring decisions—final decisions are made exclusively by our recruiting and hiring teams.
Salary Context
This $159K-$238K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Included 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($198K) sits 9% below the category median. Disclosed range: $159K to $238K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Included Health AI Hiring
Included Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $238K - $238K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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