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 R37R37 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+ claims550M+ patient encounters1.2B+ workflow actions and outcomes annuallyThis 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.The RoleWe are looking for an Applied AI Engineer/Scientist to build, evaluate, and continuously improve clinical AI agents and supervised ML Models.You will work at the intersection of software engineering, LLM systems, evaluation, model improvement, and deep healthcare workflow understanding. Your job is to turn frontier model capability into reliable production behavior: agents that read complex medical records, use the right clinical and coding context, call the right tools, produce auditable outputs, and improve from real-world failures.You will be embedded in hard healthcare problems — clinical documentation integrity, medical coding, denial prevention, appeals, revenue cycle workflows, and payer logic — and will own the loop from problem framing to agent design, evaluation, deployment, trace analysis, and ongoing improvement.The ideal candidate is a strong engineer who thinks like an applied scientist: rigorous about measurement, comfortable with ambiguity, excited by messy real-world data, and motivated by closing the gap between impressive demos and dependable production systems.We prefer this role to be Hybrid (3 days onsite) at our SOHO Office Hub in New York City.What You'll DoDesign, 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. What Makes This Role DifferentMost AI roles are either too research-heavy or too product-light. This role sits in the middle.You will not only write prompts or run experiments. You will own whether an agent actually works in production. That means understanding the workflow, designing the system, building the evals, inspecting failures, improving the agent, and proving that the improvement matters. The right person will be excited by questions like:What context does this agent need to make the right decision?How do we know the output is clinically and operationally correct?Which failures are prompt problems, retrieval problems, model problems, tool problems, or product-spec problems?How do we turn expert feedback into a bet