Vulnerability Disclosure at R37 Lab, R1 RCM

Job description

The full description lives on the employer’s site. Open the listing.

Impact at Scale: Employees work on problems that directly affect the financial health of the top healthcare systems in the US, improving care delivery nationwide.
Rigorous R&D Culture: The lab is described as a "dedicated lab for agentic innovation" where research is rapidly translated into production-grade products. This appeals to engineers and scientists who want to see their work have a real-world impact.
Flexible Work: The lab appears to offer a mix of remote and on-site roles based in New York, NY. Job listings specify both "Remote" and "On-site" options. jobs.ashbyhq.com
Cutting-Edge Technology: Engineers get to work on challenging problems in scalability, latency, reliability, and security within a regulated environment, deploying models like LLMs, voice models, and computer-use models.

One-liner: R37 is the applied AI lab within R1 RCM, building an enterprise-grade AI layer to automate and transform the healthcare revenue cycle.
Entity Type: Internal lab of a public company (R1 RCM, NASDAQ: RCM)
Headquarters: New York, NY, USA (with a presence in London, UK following the Phare Health acquisition)
Founded: 2025
Founders: The lab was founded internally by R1 RCM. Co-CEOs of the lab are Martin Seneviratne and Lee Kupferman, who joined through the acquisition of their company, Phare Health.

Primary industry: Healthcare Revenue Cycle Management (RCM) / Enterprise AI
Target customers: Enterprise (top US health systems), B2B
Mission/purpose: To build AI that makes healthcare reimbursement fair, transparent, and seamless.

Phare Operating System (Phare OS): Healthcare’s first Revenue Operating System. An enterprise-grade AI platform that orchestrates agentic and labor solutions across the entire revenue cycle. r1rcm.com
Phare Claim: The clinical and claim intelligence engine of the Phare OS. It autonomously codes encounters, detects claim errors, and corrects them before submission. Known for achieving autonomous coding accuracy rates as high as 97% for service lines like emergency room visits. r1rcm.com
Phare Flow: The autonomous follow-up module of the Phare OS. It uses AI agents to follow up on every claim, from status checks to appealing clinical denials. Aiming to drive open AR down to zero. r1rcm.com
Phare Access: The smart patient access hub of the Phare OS. It blends AI and patient tools to clear patients, verify insurance, secure authorizations, and embed accurate patient data at intake. r1rcm.com

Valuation/Market Cap: Not applicable (internal lab). R1 RCM (parent company) is a publicly traded company.
Key Metric: R1’s network serves 95 of the top 100 US health systems, processing over 670 million annual patient encounters. R37 has over 1,000 employees. r1rcm.com
Notable Investors/Partners: The lab is wholly owned by R1 RCM. Key technology partners are the major Electronic Medical Records (EMR) systems and over 1,000 payers.
Growth Signals: Rapid growth since its launch in 2025. The lab acquired Phare Health in October 2025 to bolster its AI-native inpatient coding capabilities. The Phare OS is already in production with select customers, and the lab is actively hiring for roles like Applied AI Scientist, Engineering Manager, and Research Scientist. r1rcm.com

Scale and Data Advantage: Unparalleled access to a massive, real-world dataset from serving the largest US health systems. This provides a significant moat for training and deploying specialized AI models.
System-Level Approach: Unlike competitors that solve isolated problems, R37 is building a holistic, orchestrated AI platform (Phare OS) that manages the entire revenue cycle, from patient intake to final payment.
Deep Domain Expertise: The team combines world-class AI talent with two decades of revenue cycle expertise from R1, creating a deep understanding of both the technology and the complex regulatory environment.

AI-Native RCM: The current priority is to transition the revenue cycle from a labor-intensive model to an AI-native, autonomous system.
Real-Time Adjudication: A long-term goal is to make real-time claim adjudication an operational reality, eliminating the current delays in the payment process.
Agentic AI: The lab is heavily focused on building and deploying AI agents that can autonomously handle complex workflows, such as appealing denials and coding inpatient encounters.

Sources