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About This Role
Head of AI Operating System
*Raintree • Reports to CEO • Remote (United States) • Full\-time*
Build the Agentic AI Operating System of the Next SaaS Era
This is not a job for the operator who wants to manage from the executive deck. It is a job for the rare person who can sit at the executive table on Friday morning, design an autonomous\-agent architecture that afternoon, ship the first working version of it that week, and have it generating measurable business impact by the end of the quarter.
Raintree is investing now to build what we believe will define the next generation of operating SaaS companies: a fully internal, agentic AI Operating System that runs across every function of our business. Every workflow that can be safely automated, will be. Every decision\-support layer that can be enriched by frontier models, will be. Every system of record we run on — Salesforce, Slack, Gong, our ticketing systems, our finance and close stack, and the rest — will be wired together through MCP and a coherent semantic layer so agents can act with context, not guesswork. The semantic and data layer that makes this possible is not a supporting concern — it is the foundation, and building it in partnership with our engineering organization is the enabling condition for everything else.
We are looking for the founding builder of that system. You will be a pioneer of what it means to operate a SaaS business as an AI\-native company — not by talking about it, but by shipping it.
About Raintree
Raintree is the leading enterprise SaaS platform serving the physical therapy, occupational therapy, and speech therapy industries. Our integrated EMR, practice management, and billing solutions power many of the largest therapy organizations in the country. We are profitable, growing, and at a pivotal moment of AI\-driven reinvention.
You do not need to come from healthcare. You need to come ready to learn a business fast and to ship.
The Role
You will report directly to the CEO and own the internal Agentic AI Operating System for Raintree end\-to\-end. This role is deliberately distinct from AI in our product — customer\-facing AI features in the Raintree platform remain owned by our product and engineering organization, and core engineering productivity and the AI Product Development Lifecycle are owned by our engineering leadership. *Your customer is Raintree itself, and your remit is every other function of the business.*
You will lead a cross\-functional working group of departmental representatives — "AI champions" embedded inside Customer Success, Support, Revenue, RCM Operations, Finance, People, and IT — who report into their functional leaders but partner with you to identify, build, deploy, and scale AI inside their domains. This hub\-and\-spoke model is how we ensure what we build moves fast, sticks, and produces durable EBITDA improvement while simultaneously improving customer NPS company\-wide. This person’s success depends on ELT commitment to implementation — securing and maintaining that alignment across the C\-suite is as much a part of the job as the technical work.
You will be measured on outcomes, not activity. Your variable compensation is tied directly to validated EBITDA contribution and customer NPS lift — see the KPI section below.
What You Will Do
Lead at the ELT level
- Partner with the CEO and ELT to define Raintree's internal AI strategy and operating priorities; defend the roadmap with rigorous ROI analysis.
- Build and chair an executive AI council that sets policy, approves investments, and unblocks cross\-functional work.
- Run the AI champions network — a working group of departmental representatives — as the operating engine of the program.
- Brief the board on AI program health, financial impact, and forward investment plan on a quarterly cadence.
- Navigate cross\-functional alignment without direct authority — earn the credibility with C\-level peers to move agendas through them, not around them; this role succeeds or fails on the strength of those working relationships.
Build, with your own hands
- Architect and ship agentic workflows on frontier models (Claude, GPT, Gemini, leading open source) that automate or substantially augment real work across the business outside of core engineering.
- Build the semantic and data layer that is the foundation of the entire program — the production\-grade context pipeline across Raintree’s systems of record that allows agents to act with precision rather than guesswork. This is a primary deliverable, not a supporting one, and requires active partnership with engineering leadership to get the AI infrastructure right.
- Build MCP\-based integrations into Salesforce, Slack, Gong, our ticketing systems, our finance and close tooling, and the long tail of internal SaaS — so agents can read, write, and act safely with permissioned scope.
- Establish the evaluation, observability, and red\-team practices that keep agents performant and safe in production.
- Prototype quickly, ship pragmatically, and migrate proven work to the teams that will own it long\-term — without losing operational control.
Govern the program
- Build Raintree's AI governance framework: how new AI tools are evaluated, approved, procured, deployed, monitored, and retired.
- Define data\-handling policy for HIPAA, PHI, and patient\-data sensitivities; partner closely with Security, Legal, and Compliance.
- Contain shadow AI; rationalize the vendor portfolio; negotiate enterprise contracts.
- Stay ahead of the regulatory landscape (OCR/HIPAA AI guidance, state\-level healthcare AI rules, EU AI Act spillover) and translate it into operating practice.
Track, validate, and report ROI
- Stand up the dashboard that shows the CEO and CFO, in one place: AI spend by tool and team, adoption, project status, validated EBITDA impact, and customer NPS movement.
- Co\-own ROI validation methodology with Finance — every initiative gets a baseline at intake and a post\-launch measurement.
- Hold initiatives accountable to their committed outcomes; kill what does not work.
How Your Performance Is Measured
Raintree is in the middle of a meaningful EBITDA expansion initiative, and the AI Operating System is one of the most important levers in delivering it. This role exists to enable a significant portion of that lift while simultaneously improving customer NPS.
Your variable compensation is tied to a scorecard of outcome KPIs, validated by Finance and reviewed quarterly with the CEO. Target bonus is 35–40% of base salary, with payout scaled from a 50% floor at threshold to 150% at stretch.
Scorecard structure
- 60% — Validated annualized EBITDA contribution. Run\-rate EBITDA improvement attributable to AI initiatives in your portfolio, baselined at intake and post\-launch validated by Finance. Specific dollar targets are agreed at hire and refreshed annually with the CEO and the Board.
- 25% — Adoption and customer NPS impact. Active\-user adoption of approved AI tools across in\-scope functions, plus measurable customer NPS lift attributable to AI\-augmented service quality and operational improvements.
- 15% — Governance and program health. Zero material AI\-related security or compliance incidents; 100% of in\-scope AI tools through documented review before broad rollout; AI champions network operational across all in\-scope functions; vendor portfolio rationalized.
How EBITDA and NPS impact are validated
- Baseline established jointly with the functional owner and Finance before launch; customer NPS baselines captured for affected service touchpoints.
- Post\-launch measurement at 90 and 180 days; annualized run\-rate locked at 180 days.
- Categories that count for EBITDA: labor capacity unlocked, cycle\-time reduction with quantified downstream effect, vendor\-spend reduction, churn / retention improvement, and revenue impact directly attributable to AI\-augmented motions.
- Soft wins do not count. If Finance cannot sign it, it does not score.
Who We Are Looking For
This is an athlete role. We are not looking for the candidate with the longest title or the most reports. We are looking for someone with extraordinary raw capability, intellectual horsepower, and the rare combination of executive presence and hands\-on build instinct. The person who has the academic and analytic foundation, the AI fluency, and the hunger to be a pioneer.
Profile
- Top\-tier academic background — leading university and quantitative discipline (Computer Science, Engineering, Mathematics, Physics, Economics, or similar). Graduate degree is a plus, not a requirement.
- Roughly 6–10 years of progressive experience, ideally a combination of: technical AI/ML work, top\-tier strategy consulting (MBB) or operating role at a high\-performance SaaS company, and at least one role where you built something from zero.
- You are currently in a senior IC or director\-track role at a strong company — not yet in a Head of / VP seat where you have stopped touching the work. This role is your step up, and you are hungry for the platform.
- Business mind first, AI specialist second. You think in P\&L, ROI, EBITDA, and customer NPS. You can read a financial model and challenge it. You do not get lost in technology for its own sake.
- Healthcare experience is welcome but not required. You can learn a business quickly.
Technical depth
- Real, recent, hands\-on expertise applying frontier models (Claude, GPT, Gemini, leading open source) in production — not just experimenting in notebooks.
- Deep fluency with agentic workflows: planner\-executor patterns, tool use, evaluation harnesses, observability, guardrails, human\-in\-the\-loop design.
- Working knowledge of MCP (Model Context Protocol) and a track record integrating LLM systems with Salesforce, ticketing, Slack, Gong, finance and close tooling, and similar SaaS surfaces.
- Comfort designing and building semantic layers, retrieval architectures, and the data plumbing that makes agents reliable.
- Practical understanding of AI security, privacy, and risk: prompt injection, data exfiltration, audit logging, red\-teaming, and HIPAA/PHI handling.
Operating style
- Builder's bias. Ships working software, not slide decks.
- Comfortable moving between the boardroom and the IDE in the same day.
- Allergic to AI theater; calibrated about what works and what is hype.
- Operates with conviction and welcomes accountability — variable comp tied to validated outcomes should feel like an opportunity, not a threat.
- Force multiplier — gets work done through a network of champions, not by hoarding scope.
- Organizational orchestrator — can align a network of senior stakeholders around a coherent program agenda without direct authority; gets functional leaders to commit their teams to execution, not just agree in meetings.
Success in Your First 12 Months
- By day 30: Raintree\-wide AI landscape assessment complete; the highest\-ROI opportunities identified and baselined.
- By day 60: AI governance framework and a prioritized 18\-month AI roadmap approved by the CEO and ELT; AI champions network stood up across in\-scope functions.
- By day 90: First two production agentic workflows shipped, with baseline\-validated EBITDA impact and customer NPS instrumentation in flight; spend, adoption, and ROI dashboard operational.
- By month 6: Semantic layer and MCP integration backbone in production; four to six high\-ROI agentic workflows live; meaningful validated EBITDA run\-rate achieved while simultaneously improving customer NPS.
- By month 12: Year 1 EBITDA and customer NPS targets achieved; trajectory locked in to deliver materially larger run\-rate impact in the second year as a primary contributor to Raintree's margin expansion; the AI Operating System is the way Raintree runs.
Compensation: Base salary $220,000 to $250,000, depending on experience. Target annual bonus 35–40% of base (approximately $80,000 to $100,000 target), scaled from a 50% floor at threshold to 150% at stretch against the scorecard above. Equity / long\-term incentive participation. Total target cash $300,000 to $340,000; total compensation $360,000 to $420,000 at stretch.
Benefits: Comprehensive medical, dental, vision, 401(k), and a fully remote\-first culture.
Why This Role, Why Now
The companies that win the next decade of SaaS will not be the ones that bolt AI features onto their products. They will be the ones that rebuild themselves, from the inside out, as agentic AI\-native businesses. Raintree intends to be one of them. The Head of AI Operating System is the person who makes that real, and who delivers measurable EBITDA improvement while simultaneously improving customer NPS as proof of it.
If you are an athlete — extraordinary intellect, top\-tier credentials, deep AI fluency, builder's hands, and the hunger to pioneer something foundational — we want to talk to you.
How to Apply
Please send a resume and and cover letter describing: (1\) the most ambitious AI system you have personally built and shipped, (2\) the measurable business outcome it produced, and (3\) why you are the right person to build the Agentic AI Operating System of the next SaaS era at Raintree. We read every submission carefully.
*Raintree Systems provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
About Raintree Systems
Raintree is the preeminent provider of electronic health records (EHR) and revenue cycle management (RCM) software for the therapy and rehabilitation industry. Founded in 1983 and headquartered in Chandler, AZ, Raintree serves enterprise and mid\-sized organizations across physical therapy, occupational therapy, speech\-language pathology, and ABA specialties. Our award\-winning, all\-in\-one platform empowers therapy professionals to deliver superior patient care through innovative clinical documentation, automated billing, and actionable business intelligence. With over 2,500 implementations and a commitment to "Software\-as\-a\-Relationship," we are a mission\-driven team dedicated to transforming healthcare technology and improving outcomes for everyone.
Our Core Values
We put our Clients First \- We are Open and Honest \- We are Disciplined, Yet Flexible
We love to Solve Problems \- We are Committed to Greatness \- We are a High Performance Team
*Raintree Systems provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
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Salary Context
This $220K-$420K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Raintree Systems, Inc., 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($320K) sits 79% above the category median. Disclosed range: $220K to $420K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Raintree Systems, Inc. AI Hiring
Raintree Systems, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $420K - $420K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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