Product Manager

Layer Health•Boston, MA

1d•$110,000 - $160,000

About The Position

Layer Health is seeking a curious, technically-minded Product Manager to contribute to the development and launch of features for their clinical intelligence platform. This role involves close collaboration with product, design, engineering, and research teams to translate advanced LLM capabilities into practical tools for clinical settings. As an early hire in the product team, the Product Manager will have significant opportunities for skill development, increased responsibility, and shaping product development processes alongside the founding team.

Requirements

2–4 years of Product Management experience, ideally in B2B software, health-tech, or another complex, high-stakes domain.
Technically Curious: Comfortable working with engineers and researchers; exposure to AI/ML products or a strong interest in learning; understanding of the iterative nature of shipping LLM-powered features, including evaluation and attention to edge cases.
Structured Thinker: Ability to break down ambiguous problems, write clear specifications, and coordinate across teams. Familiarity with data systems, APIs, or enterprise software integrations is helpful.
Early-Stage Mindset: Eagerness to wear multiple hats, move quickly, and grow into broader ownership as the company scales.

Nice To Haves

Prior health-tech experience is a plus, but not required—we value curiosity and the ability to learn quickly from users.

Responsibilities

Contribute to the AI UX by partnering with design and engineering to define how healthcare professionals interact with AI, ensuring LLM outputs are clear, intuitive, and actionable.
Learn clinical workflows by engaging with clinicians and nurses to understand their daily tasks, identify pain points, and determine how Layer Health's platform can reduce manual effort.
Support data integration by working with engineering and customers to understand product connections within the healthcare ecosystem (EHRs like Epic and Cerner, HL7/FHIR standards, clinical data pipelines) and prioritizing integration efforts.
Ship product features by owning features from discovery to launch, collaborating with engineering to deliver reliable AI-powered experiences in production environments where accuracy and trust are critical.
Close the feedback loop by collecting and synthesizing clinician feedback, collaborating with research on model evaluation, and translating learnings into product improvements.