Applied AI Engineer (R37)

R1 RCM•Remote, NY, NY

1d•$140,000 - $270,000•Remote

About The Position

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. 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.

Requirements

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.

Responsibilities

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.