Research Scientist

About Us

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, including:

• 180M+ Claims

• 550M+ Patient encounters

• 1.2B+ Workflow actions and outcomes 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.

The Role

We skew toward the RS/RE blend – applied scientists who feel comfortable getting close to production code. 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 ML Ops 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, requiring at least 3 days in the SoHo office.

About you

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

Additionally, 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)

Role Leveling

We are looking for candidates at various levels with this type of experience, ranging from L2 (Mid-Level) to L4 (Staff).

• L2: Independently delivers a complete end-to-end project, owning design, implementation, and delivery of scoped work

• L3: Leads delivery of larger projects, handling increased technical complexity and ambiguity, providing light guidance to L2s on shared work

• L4: Leads teams or major cross-functional initiatives while managing a portfolio of projects aligned to key technical goals

Benefits

• Top-of-market compensation (salary + equity)

• Flexible PTO

• Hybrid in-office (min. 3 days per week)

• Comprehensive health benefits

• 401(k) matching

• Inspiring, brilliant, mission-driven teammates

Hiring Flow

• Intro call - your background & our mission alignment

• Technical deep-dives - pseudo-coding exercise and systems design (not Leetcode)

• Culture interview in person in NYC

• References

• Offer

Interview Logistics Notice

As part of our hiring process, selected candidates will participate in an in-person interview. Candidates located near one of our talent hubs—San Francisco, New York, Austin, or Chicago—will be scheduled to meet with team members in those locations. For candidates residing outside these areas, we will arrange travel to a hub for the interview. Travel accommodation will be provided as needed. We are committed to providing equal employment opportunities and ensuring a fair and inclusive experience for all applicants.

For this US-based position, the base pay range is $140,000.00-$300,000 per year. Individual pay is determined by role, level, location, job-related skills, experience, and relevant education or training.

This job is eligible to participate in our annual bonus plan.

The healthcare system is always evolving — and it’s up to us to use our shared expertise to find new solutions that can keep up. On our growi