Phare is building healthcare's first Revenue Operating System using AI to make hospital billing and reimbursement effortless, accurate, and fair.
Requirements
- 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).
Responsibilities
- Conduct applied research on real healthcare data, focused on explainability, reinforcement learning, and long-context information retrieval.
- 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.
- Stay close to the literature and close to production, coding your own experiments end to end.
- Design novel architectures and pipelines in PyTorch/TensorFlow/JAX.
- Run large-scale training and evaluation on distributed infrastructure (e.g., Ray, FSDP, Lightning).
- Publish research at top ML venues (e.g., NeurIPS, ICML, EMNLP, MLHC, CHIL).
Other
- This is an in-person role in NYC requiring at least 3 days in the SoHo office.
- You have a PhD in computer science / informatics plus at least 3 years of industry experience (will consider postdocs also).
- Hybrid in-office (min. 3 days per week)
- Culture interview in person in NYC
