Research Scientist

Layer Health is an AI startup spun out of MIT. We leverage LLMs to vastly improve the value and impact that can be unlocked from medical record data - building the "AI layer" for healthcare. We know that all organizations in the healthcare ecosystem struggle to extract and synthesize insights from medical records at scale. Our AI layer radically speeds up and improves the quality of information gleaned from these records to power a wide range of impactful use cases- from research to quality measurement to clinical care pathways. Our team consists of world-class experts across machine learning, LLMs, medicine, engineering, and commercial execution. We are a Series A company headquartered in Boston, with offices in both Boston and NYC. We’re seeking outstanding hires to join our team as early members. This is an opportunity to contribute to a high-impact, collaborative, mission-driven team, and help define the next stage of growth for Layer Health. Together, we will create the AI layer that will redefine healthcare for the better. Job Description We’re hiring an exceptional research scientist. In this role, you will be responsible for pioneering innovative machine learning techniques to advance our fundamental clinical machine learning and large language model efforts. You can expect to: Design and implement state-of-the-art machine learning techniques to advance Layer Health’s research agenda (in areas such as information extraction, multimodal reasoning, and summarization). Propose new agentic methods that tackle fundamental NLP and ML challenges such as modeling over multiple documents, long contexts, multiple modalities, and with limited or noisy labels. Build foundation models to power the future of clinical information extraction & synthesis, from training through inference. Stay up-to-date and actively engage with cutting-edge research in NLP, generative AI, and clinical machine learning. Collaborate with the broader engineering team to ship performant products that meet user needs. Cultivate and foster a robust and thoughtful R&D culture that drives the company forward. We look for: Exceptional methodological research background and experience, including but not limited to: A PhD in computer science/applied mathematics or equivalent research experience, specializing in natural language processing and machine learning. High-impact, early-author publications at top peer-reviewed ML journals/conferences. Demonstrated record of delivering real-world impact from start to finish - with the ability to design, develop, and ship innovations. Strong programming skills and fluency with modern machine learning/LLM stacks (deep learning libraries e.g. PyTorch, Jax). Past experience in training/inference of foundation models (billions of parameters, distributed training, familiarity with state-of-the-art techniques). A strong communicator who thrives in a customer-focused, fast-paced environment. An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team. Past experience in healthcare or life sciences is a plus, but not required. We are a Boston-based company, and expect employees to meet regularly in-person in Boston (employees from Boston, NYC, or east coast are welcome). Join us and help us transform healthcare with AI. Layer Health is committed to fostering an environment of inclusion that is free from discrimination. We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law. Expected compensation range for this role is $200K-250K for Boston or NYC-based candidates; range may vary for candidates outside of the NYC/Boston metro area. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. #J-18808-Ljbffr