Research Scientist (AI)

TL;DR

Research Scientist (AI/ML): Developing an AI-native Healthcare Revenue Operating System with an accent on explainability, reinforcement learning, and long-context retrieval. Focus on designing novel architectures, training models at scale, and deploying research-driven solutions into production healthcare workflows.

Location: In-person in NYC, requiring at least 3 days in the SoHo office

Salary: $175,000 – $300,000

Company

hirify.global (part of R1 and R37 Lab) is building an AI-native Healthcare Revenue Operating System to automate medical coding, billing, and financial workflows on a national scale.

What you will do

• Conduct applied research on real healthcare data with a focus on explainability and reinforcement learning.
• Design and implement novel architectures and pipelines using PyTorch, TensorFlow, or JAX.
• Execute large-scale training and evaluation using distributed infrastructure such as Ray, FSDP, and Lightning.
• Collaborate with ML Ops and Product teams to transition research concepts into production-ready features.
• Stay current with the latest ML literature to solve complex clinical reasoning challenges.

Requirements

• PhD in Computer Science or Informatics.
• At least 3 years of industry experience (postdoctoral experience considered).
• Research expertise in interpretability, reinforcement learning, RAG, or long-context information retrieval.
• Proven track record of publications at top ML venues like NeurIPS, ICML, EMNLP, MLHC, or CHIL.
• Must be based in or able to work from New York City.

Nice to have

• Experience with applied research that has successfully transitioned into a production environment.

Culture & Benefits

• Top-of-market compensation package including salary and equity.
• Flexible PTO and comprehensive health insurance.
• 401(k) matching.
• Hybrid work model (minimum 3 days per week in NYC).
• Collaborative environment with a mission-driven team.

Hiring process

• Introductory call focused on background and mission alignment.
• Technical deep-dives featuring pseudo-coding and systems design.
• In-person culture interview in NYC.
• Reference checks.