Senior Product Manager

RapidAI is the trusted leader in deep clinical AI, helping hospitals deliver faster, more informed care through intelligent imaging and integrated workflows. The Rapid Enterprise™ Platform supports disease states across the care spectrum, but it’s our clinical depth that drives the most meaningful impact — improving decision-making, patient outcomes, and health-system performance. Used by more than 2,500 hospitals in over 100 countries and backed by 700+ clinical studies, including research that helped expand national stroke-treatment guidelines, RapidAI is the most clinically validated AI platform in healthcare.

How you help us grow!
Position Duties and responsibilities: Own the outcome: Set the direction, roadmap, and priorities for the apps. When building is cheap, deciding what not to build matters more, not less: say no to good ideas that are not the most important thing. Tie each initiative to a result you can measure, such as time from scan to care-team notification or adoption across a health system’s sites, and report what actually happened after it shipped. Know the workflow first-hand: Spend regular time with the people who use the apps (neurologists, radiologists, emergency physicians, stroke and PE coordinators, nurses) and in the workflows around them: stroke codes, PE response team activations, transfers between hospitals. Turn what you see into clear problem statements. Prototype before you specify: Build working prototypes yourself with AI coding and design tools, put them in front of clinicians within days, and bring evidence rather than a document to the decision to commit engineering time. Write requirements engineering can build from: Write your own epics and user stories, with testable acceptance criteria, edge cases, error states, and analytics events. Use AI to draft, challenge, and check them, and own every word: at a medical device company, requirements are design inputs that must be unambiguous, verifiable, and traceable to user needs and risk controls. If a coding agent cannot build from a spec, the spec is not finished. Bring AI to clinicians’ problems: Beyond the imaging AI modules, find where language models and agents can take work off a care team’s plate, such as pulling the right context from the EHR, drafting documentation, or getting the right information to the right person. Define what good looks like before building, in evals as well as acceptance criteria, and design for the moments the model is wrong. Treat notification reliability as patient safety: A late or missed notification can delay treatment, and too many notifications teach clinicians to ignore them. Own notification behavior, thresholds, and settings with both risks in mind, and partner with Platform and Engineering on delivery, monitoring, and supportability. Turn repeated work into reusable patterns: Every new AI module, including partner algorithms, reaches clinicians through the same building blocks: notifications, patient list cards, case summaries, the status feed, and settings. Define and maintain these as patterns so that bringing the next module to the apps is configuration, not a project. Ship safely in a regulated environment: Work within our design-control process alongside Quality, Regulatory, and Clinical: contribute to risk analysis and usability engineering, support verification and validation, and own release readiness for your area. Move fast where it is safe (discovery, prototyping, analysis) and hold the line where it matters (design inputs, risk controls, verification, protected health information). Recognize which changes need a regulatory assessment, and raise them early. Work as one team with Design and Engineering: Be in the problem with designers and engineers from the start, and stay available for the many small decisions that come up mid-sprint, including across time zones. Expect roles to overlap: you will build, and designers and engineers will make product calls. Communicate decisions in writing: Share progress as working demos where you can, and write roadmap updates, decision records, and release notes that executives and clinical, sales, and support teams can act on. Bring product expertise to key customer conversations, and equip product marketing and customer success with what shipped and why it matters; positioning and sales materials belong to product marketing. Push the team’s AI practice forward: Improve the agents and workflows the product team runs on, automate your own repetitive work, share what works, and mentor other product managers by example.

What you bring!
Required 5+ years of software product management experience, including ownership of a mobile or web product that professionals rely on in their daily work. Bachelor’s degree in engineering, computer science, life sciences, nursing or another health profession, business, or a related field, or equivalent experience. AI built into daily work: uses AI through repeatable workflows, not one-off prompts, for research synthesis, requirements, analysis, and prototyping, and can show how it changed the speed or quality of the work. Trajectory matters as much as current skill: what was tried, kept, and dropped. Hands-on building: has built working prototypes or tools with AI coding tools and used them to test ideas with real users. Product judgment and taste: when building is cheap, choosing what to build is the job. Prioritizes with incomplete information, states assumptions, and changes course when the evidence does. A record of writing requirements that engineers build from directly: precise, testable, and complete on edge cases. Depth in mobile products: iOS and Android conventions; how push notifications are delivered and where delivery fails (OS settings, Focus and Do Not Disturb, critical alerts); behavior on poor connectivity; and release mechanics (app store review, staged rollout, users on old versions). Technical fluency to discuss APIs, data models, and system design with engineers, and to work through trade-offs with them rather than hand problems over. Clear, concise writing, and the ability to explain the same decision to a clinician, an engineer, and an executive. Ownership: follows a problem across team boundaries until it is solved, and owns AI-assisted work as fully as any other: defines what good looks like up front, checks output critically, and catches errors before they reach anyone else. Preferred Experience building for clinicians, particularly in acute care (stroke, emergency medicine, critical care, radiology), or a clinical background such as nursing. Experience shipping software as a medical device or in another regulated environment; working knowledge of FDA design controls, ISO 13485, IEC 62304, ISO 14971, and IEC 62366. Experience shipping AI-powered features: writing evals, judging model output quality, and designing for when the model is wrong. Has built agents, automations, or skills that other people use in their daily work. Experience with real-time messaging, alerting, or care-team communication products. Familiarity with healthcare data and interoperability: DICOM, HL7, FHIR, and HIPAA. Product analytics experience: event instrumentation, funnels, and cohort analysis.

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