Research Scientist (R37)

R1 RCM•Remote, NY, NY

1d•$140,000 - $270,000•Hybrid

About The Position

As our Research Scientist, you will help advance the state of applied AI in healthcare by developing novel machine learning approaches that improve how intelligent systems reason, retrieve information, and explain decisions. Every day, you will design and run experiments on large-scale healthcare datasets, develop and evaluate cutting-edge models, and collaborate with engineering and product teams to bring research innovations into production. To thrive in this role, you must be deeply grounded in machine learning research, enjoy working hands-on with production systems, and be excited about turning breakthrough ideas into measurable real-world impact. Here's what you will experience working as a Research Scientist: Conducting applied research in areas such as explainable AI, reinforcement learning, retrieval-augmented generation (RAG), and long-context information retrieval. Designing, training, and evaluating novel machine learning models using large-scale healthcare data and distributed computing infrastructure. Partnering closely with ML Engineering, ML Ops, and Product teams to move research innovations from concept to production. Building end-to-end research prototypes, experimentation frameworks, and validation methodologies that deliver measurable business and user impact.

Requirements

PhD in Computer Science, Computer Engineering, Informatics, Machine Learning, Artificial Intelligence, or a related field.
3+ years of industry research experience (postdoctoral experience may be considered equivalent).
Expertise developing machine learning models and architectures using frameworks such as PyTorch, TensorFlow, or JAX.
Demonstrated research experience in one or more of the following areas: interpretability, reinforcement learning, retrieval-augmented generation (RAG), or long-context information retrieval.
Experience training and evaluating large-scale machine learning models on distributed computing platforms such as Ray, FSDP, or PyTorch Lightning.
Proven publication record at top-tier machine learning, AI, or healthcare AI conferences such as NeurIPS, ICML, EMNLP, MLHC, or CHIL.

Responsibilities

Conducting applied research in areas such as explainable AI, reinforcement learning, retrieval-augmented generation (RAG), and long-context information retrieval.
Designing, training, and evaluating novel machine learning models using large-scale healthcare data and distributed computing infrastructure.
Partnering closely with ML Engineering, ML Ops, and Product teams to move research innovations from concept to production.
Building end-end research prototypes, experimentation frameworks, and validation methodologies that deliver measurable business and user impact.