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About This Role
At R1, we’re transforming how healthcare works by combining nearly two decades of revenue cycle expertise with advanced technology, including analytics, AI, intelligent automation and workflow orchestration. We pair that technology with the irreplaceable expertise of our people to help hospitals, health systems and medical groups improve financial performance, simplify administrative complexity and create better experiences for patients and providers.
All Together Better defines our culture and the way we work. We’re a diverse, global team united by a shared mission to make healthcare work better for all. Across technology, operations and support functions, our people bring deep expertise, empathy and ingenuity to the work we do. Here, collaboration fuels progress, adaptability sparks innovation and every voice can help shape what’s next for R1 and the future of healthcare.
About R37
R37 is R1’s AI innovation lab, building technology to transform healthcare.
Our flagship platform, Phare OS, uses AI and automation to improve critical pre-bill workflows, including authorization, utilization management, documentation and coding. By identifying and resolving issues before claims are submitted, Phare OS helps prevent denials, improve reimbursement and move revenue cycle work upstream.
What makes R37 different is the opportunity to build advanced AI at real-world scale. R1 works across 95 of the top 100 U.S. health systems, giving R37 access to the data, workflows and reach needed to make an immediate impact:
- 180M+ claims
- 550M+ patient encounters
1.2B+ workflow actions and outcomes annually
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This is startup-level ownership with enterprise-level impact. You’ll build technology that moves from idea to real healthcare environments, solving complex problems at scale and changing how healthcare works.
As our Applied AI Scientist, you will help transform complex healthcare workflows into reliable, production-ready AI solutions that improve clinical and revenue cycle outcomes. Every day you will design and optimize AI agents for healthcare use cases, evaluate and improve model performance through rigorous testing and feedback loops, and collaborate with clinical, product, and engineering teams to translate real-world challenges into scalable AI systems.
To thrive in this role, you must be highly proficient in Python, passionate about building dependable AI systems, comfortable navigating ambiguity, and excited about solving complex healthcare problems using agentic AI, machine learning, and LLM technologies.
Here's what you will experience working as an Applied AI Scientist:
- Design, build, and iterate on agentic AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation.
- Develop long-horizon agent behavior across context construction, retrieval, tool use, memory, routing, verification, escalation, and human-in-the-loop review.
- Define what “good” looks like for clinical agents end-to-end, translating expert workflows into specifications, rubrics, gold standards, test cases, and clinically meaningful success criteria.
- Build rigorous evaluation and feedback loops using expert review, production logs, model outputs, and benchmarks to measure performance, regressions, edge cases, safety, reliability, provenance quality, and business impact.
Prototype new AI capabilities from 0* 1, then harden them into reliable, explainable, auditable production systems with clear contracts, monitoring, evidence, rationale, and performance gates.
Partner with research and ML engineering teams on model selection, fine-tuning, reward modeling, distillation, synthetic data, post-training, and internal AI infrastructure, including instrumentation, experiment tracking, benchmarking, prompt/version management, and reproducible evaluation.
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Required Skills:
- 4+ years of experience in software engineering, machine learning engineering, applied AI, research engineering, or a related field.
- Strong proficiency in Python and experience building production applications using APIs, structured data, testing, logging, and observability tools.
- Experience with LLM applications, AI agents, RAG (Retrieval-Augmented Generation), prompt engineering, tool calling, structured outputs, or machine learning systems.
- Experience designing evaluation frameworks, benchmarks, experimentation workflows, and performance monitoring for AI systems.
Ability to work in ambiguous, high-impact environments and translate complex real-world problems into scalable AI solutions.
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Role Leveling
We are looking for candidates at various levels, ranging from Level 2 to Staff
- L2: Independently delivers a complete end-to-end project, owning design, implementation, and delivery of scoped work (Compensation Range for L2: $140k – $200k)
- L3: Leads delivery of larger projects, handling increased technical complexity and ambiguity, providing light guidance to L2s on shared work (Compensation Range for L3: $140k – $253k)
- Senior: Team Lead responsible for managing a portfolio of projects that contribute to major technical initiatives. (Compensation Range for Senior: $140k – $294k)
- Staff: Impact at the organizational level. Responsible for leading multiple teams or multiple broad initiatives simultaneously, ensuring that high-level technical goals are met across the entire organization. (Compensation Range for Staff: $140k – $350k)
Benefits
- Top-of-market compensation, including bonus that starts at 10%
- Flexible PTO
- Comprehensive health benefits
- 401(k) matching
- Inspiring, brilliant, mission-driven teammates


For this US-based position, the base pay range is $140,000.00 - $270,000.00 per year . Individual pay is determined by role, level, location, job-related skills, experience, and relevant education or training. This job is eligible to participate in our annual bonus plan at a target of 10.00% The healthcare system is always evolving — and it’s up to us to use our shared expertise to find new solutions that can keep up. On our growing team you’ll find the opportunity to constantly learn, collaborate across groups and explore new paths for your career.
Our associates are given the chance to contribute, think boldly and create meaningful work that makes a difference in the communities we serve around the world. We go beyond expectations in everything we do. Not only does that drive customer success and improve patient care, but that same enthusiasm is applied to giving back to the community and taking care of our team — including offering a competitive benefits package.
R1 RCM Inc. (“the Company”) is dedicated to the fundamentals of equal employment opportunity. The Company’s employment practices , including those regarding recruitment, hiring, assignment, promotion, compensation, benefits, training, discipline, and termination shall not be based on any person’s age, color, national origin, citizenship status, physical or mental disability, medical condition, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status or any other characteristic protected by federal, state or local law. Furthermore, the Company is dedicated to providing a workplace free from harassment based on any of the foregoing protected categories.
If you have a disability and require a reasonable accommodation to complete any part of the job application process, please contact us at 312-496-7709 for assistance.
CA PRIVACY NOTICE: California resident job applicants can learn more about their privacy rights California Consent
To learn more, visit: R1RCM.com
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Salary Context
This $140K-$294K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At R1 RCM, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $140K to $294K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
R1 RCM AI Hiring
R1 RCM has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $294K - $294K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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