Product Manager

Layer Health was founded by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology to oncology. Our long‑term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine. We’re hiring a curious, technically‑minded Product Manager to help build and ship features on our clinical intelligence platform. In this role, you will work closely with our product, design, engineering, and research teams to turn breakthrough LLM capabilities into tools that deliver real value in clinical environments. You’ll join our product team as an early hire, with a strong opportunity to grow your skills, take on increasing ownership, and help shape how we build products alongside our founding team. You can expect to: Contribute to the AI UX: Partner with design and engineering to help shape how healthcare professionals interact with AI, making complex LLM outputs clear, intuitive, and actionable. Learn Clinical Workflows: Spend time with clinicians and nurses to understand their day‑to‑day work, document friction points, and identify where Layer Health’s platform can save hours of manual effort. Support Data Integration: Work with engineering and customers to understand how our products connect with the healthcare stack—including EHRs (Epic, Cerner), HL7/FHIR standards, and clinical data pipelines—and help prioritize integration work. Ship Product Features: Own well‑scoped features from discovery through launch, partnering with engineering to deliver reliable AI‑powered experiences in production environments where accuracy and trust matter. Close the Feedback Loop: Help collect and synthesize clinician feedback, partner with research on model evaluation, and turn learnings into concrete product improvements. 2–4 years of Product Management experience, ideally in B2B software, health‑tech, or another complex, high‑stakes domain. Technically Curious: You’re comfortable working alongside engineers and researchers. You have exposure to AI/ML products (or strong interest in learning) and understand that shipping LLM‑powered features requires iteration, evaluation, and attention to edge cases. Healthcare Interest: You’re motivated to learn clinical workflows and the realities of medical charting. Prior health‑tech experience is a plus, but not required—we value curiosity and the ability to learn quickly from users. Structured Thinker: You can break down ambiguous problems, write clear specs, and coordinate across teams. Familiarity with data systems, APIs, or enterprise software integrations is helpful. Early‑Stage Mindset: You’re eager to wear multiple hats, move fast, and grow into broader ownership as the company scales. Join us and help us transform healthcare with AI. Layer Health is committed to foster 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 characteristic protected by law. Expected compensation range for this role is $110,000-160,000 for Boston and NYC‑based candidates; range may vary for candidates outside of the Boston/NYC 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