Senior AI Software Engineer - Palantir Foundry

R1 is thrilled to introduce R37 committed to transforming healthcare financial performance, so providers can focus on delivering exceptional care. R37 is pioneering an AI-driven approach to revolutionize revenue cycle management. Today we serve 95 of the top 100 hospital systems in the US and R37 will serve as the AI platform layer delivering results for our customers. Joining R37 offers the dynamic energy of a startup, backed by solid revenue, clear business value, and strong investment support. 

Why this role exists 

R37 combines R1’s deep revenue-cycle domain expertise and massive operational dataset with Palantir’s AI capabilities to deliver agentic automation and real-time decisioning at an enterprise scale. You will be part of the engineering core that builds AI-native applications and data products inside Palantir Foundry, enabling faster, more accurate reimbursement and moving the market toward real-time claim adjudication. 

R1’s reach and data give your work uncommon leverage - 180M+ annual payer transactions, 550M patient encounters, 20K proprietary payment algorithms, and 1.2B annual workflow actions - the substrate for robust AI systems and production-scale reasoning. 

What you will build: 

You will design, implement, and operate AI systems in Palantir Foundry that ingest complex healthcare data, develop and enhance our ontology, and develop tools and systems for agentic RCM workflows (e.g., autonomous coding, denial prevention, payments). Your software will be deployed within clinical and financial operations where Foundry is already used to optimize hospital flow and predict discharges, proving real-world impact beyond prototypes. 

Here’s what you will experience working as a Senior AI Software Engineer - Palantir Foundry: 

Foundry Engineering (Core) 

• Construct Foundry Ontology & Object Views for revenue-cycle entities (encounters, claims, denials, payments), designing abstractions that downstream applications and agents can reliably consume. 

• Build and harden data pipelines that transform source EHR/RCM data into the canonical Ontology; implement data health checks, lineage, versioning, and automated releases for production reliability. 

• Own production operations: monitor refresh cadence across sources, triage build failures, and enforce release discipline (PR approvals, health SLOs) for “always on” workflows. 

Applied AI in R37 

• Design early phase AI systems (retrieval, ranking, categorization, generative features) over unstructured healthcare data; turn ambiguous business objectives into measurable outcomes and shipped models. 

• Instrument scientific rigor: define evaluation datasets, select proper metrics, architect model/data flywheels, and connect model performance to operational KPIs. 

• Operate at scale: productionize and maintain AI systems with robust observability, reliability, and cost control across cloud environments. 

Product & Collaboration 

• Work backwards from outcomes with product, operations, and domain experts; document decisions and tradeoffs; mentor engineers; contribute to hiring and culture for a high bar team. 

• Champion engineering best practices expected of senior software engineers (SDLC, CI/CD, automated testing, code reviews, documentation) to ensure secure, maintainable systems. 

To be successful in this