ML Engineer

R37 Lab, R1 RCM•New York, NY

20h•Onsite

About The Position

Phare Health is now part of R1 and its AI innovation engine, R37 Lab, bringing Phare’s frontier clinical reasoning technology together with one of the largest healthcare platforms in the U.S. At R37 and Phare, we are building the first AI-native Healthcare Revenue Operating System: a connected platform that reasons over full medical records, payer logic, and financial workflows to automate medical coding, billing, and follow-up. Backed by real customers, real data, and real distribution, we operate on a national scale. Our agentic AI systems already power production workflows across 95 of the top 100 U.S. health systems, processing hundreds of millions of patient encounters each year. This is startup-level ownership with enterprise-level impact. If you want to build AI that ships, scales, and measurably improves how healthcare works, this is the place to do it. As a ML Engineer, you’ll lead the development of early-phase, high-impact ML systems; own the internal ML dev environment (instrumentation, benchmarking, experimentation); and help bring scientific rigor into production environments, so ideas move rapidly from research to validated pipelines. You’ll partner closely with Research Scientists to make models production-ready with clear handoff contracts, performance gates, and packaging standards; and with ML Platform & Ops for safe rollouts. We are hiring across several seniority levels ranging from Mid-level up to Staff. At a minimum, we would expect 5 years of software engineering experience with 2 years of ML experience.

Requirements

5 years of software engineering experience
2 years of ML experience

Responsibilities

Build training and inference infrastructure for a fast-moving research team, including experiment tracking and benchmarking
Design and develop scalable AI systems for retrieval, ranking, categorization, and generative AI over large-scale unstructured healthcare data
Design, build, and operate AI/ML Systems end-to-end: from problem framing and model selection to production build and deployment, to ongoing improvement
Work backwards from complex business problems to define AI abstractions and system architectures that are scalable, explainable, and maintainable
Bring rigor to scientific decisions by defining the right evaluation datasets, metrics, and validation strategies tied to real outcomes
Design ranking and triage models that determine how work is routed between AI agents and human operators
Establish feedback loops and data flywheels that continuously improve model performance in production
Partner closely with software engineers, product managers, clinicians, and operators to ensure AI/ML systems deliver measurable business value
Prototype and scale new AI capabilities from 0 → 1, then harden them for real-world production use
Mentor other AI/ML engineers and promote best practices across modeling, evaluation, and deployment