Senior 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.

Here’s a collection of articles about our product, mission, recent funding round, etc.

We’re hiring an experienced, technically-grounded Senior Product Manager to lead the evolution of our clinical intelligence platform. In this role, you will be the bridge between our breakthrough LLM research and the high-stakes reality of clinical environments. You will own the product lifecycle for features that turn messy, unstructured clinical notes into high-fidelity data, ensuring our AI provides immediate, reliable value to clinicians through guidance and action. 

You’ll join our product team as a critical early hire, with a massive opportunity to shape the company's direction and culture alongside our founding team.

• Define the AI UX: Partner with design and engineering to shape how healthcare professionals interact with AI, ensuring complex LLM outputs are intuitive and actionable.
• Master Clinical Workflows: Spend time with clinicians and nurses to deeply understand their "day in the life," identifying friction points where Layer Health’s clinical intelligence platform can save hours of manual effort.
• Navigate Data Integration: Lead the strategy for how our products integrate with the existing healthcare stack, including EHRs (Epic, Cerner), HL7/FHIR standards, and clinical data pipelines.
• Ship Production AI: Move beyond the "pilot" phase to oversee the deployment of AI products into production environments where 99% accuracy isn't just a goal—it’s a requirement.
• Build Feedback Loops: Establish rigorous frameworks for model evaluation, using real-world clinician feedback to iterate on prompt engineering and model fine-tuning.

• 7+ years of Product Management experience, preferably in high-growth enterprise software or health-tech.
• AI/ML Native: You have a proven track record of building and deploying AI-powered products. You understand the nuances of LLMs and the difference between a prototype and a production-grade system.
• Healthcare Fluency: Deep experience with clinical workflows. You understand the complexities of medical charting, and the administrative burden facing providers today.
• Systems Thinker: You have experience with healthcare data systems and understand the hurdles of data interoperability and integration (EHRs, claims, or clinical data warehouses).
• Early-Stage Mindset: You are comfortable with ambiguity, move fast, and are willing to "roll up your sleeves" to do whatever is needed to ship.

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 other characteristic protected by law.

Expected compensation range for this role is $150,000-230,000 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.