Research Scientist

Phare is building healthcare’s first Revenue Operating System - a platform that uses AI to make hospital billing and reimbursement effortless, accurate, and fair. We’re part of R1, one of the largest companies managing healthcare claims which serves hundreds of systems around the country. This gives us the best of both worlds: the DNA of an AI startup paired with the infrastructure of a healthcare organization that supports care delivery every day. Together, we’re turning decades of operational expertise into intelligent systems that learn, adapt, and act - helping hospitals get paid correctly so they can focus on care. 
 

The Role 

We skew toward the RS/RE blend – applied, hands-on engineers who can genuinely push forward state of the art. You’ll conduct applied research on real healthcare data, focused on explainability, reinforcement learning, and long-context information retrieval. You’ll move quickly from concept to deployment - designing experiments, training models at scale, and collaborating with MLOps and product teams to turn ideas into measurable user impact. You’ll stay close to the literature and close to production, coding your own experiments end to end.  

This is an in-person role in NYC, with at least three days per week in the SoHo office. 

About you 

You have a PhD in computer science or informatics plus at least 3 years of industry experience (will consider postdocs also).

You have: 

• Experience designing novel architectures and pipelines in PyTorch/TensorFlow/JAX - strong preference for applied research which has made its way into production. 

• Research expertise in one or more of: interpretability, reinforcement learning, retrieval-augmented generation, or long-context information retrieval. 

• Comfortable running large-scale training and evaluation on distributed infrastructure (e.g., Ray, FSDP, Lightning). 

• Track record publications at top ML venues (e.g., NeurIPS, ICML, EMNLP, MLHC, CHIL).