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Heidi Health is building AI-powered clinical care tools that have already processed 73 million patient visits across 190+ countries. The company is now expanding into clinical trials, a fragmented space where eligibility checks, referrals, and patient matching still rely heavily on manual processes across research sites, sponsors, CROs, and health systems.
As Clinical Product Manager, you will own the product direction and strategy for Heidi's new clinical trials platform. This spans patient identification and trial discovery, eligibility and pre-screening, referral and site intake, and the workflows that life sciences organizations depend on to deliver studies. This is a hands-on individual contributor role where you'll spend significant time in the field—at research sites, with sponsor and CRO teams, with patients—observing real workflows and translating what you learn into shipped product.
Key responsibilities include: setting strategy, outcomes, and roadmap for the clinical trials product; spending time with investigators, research nurses, coordinators, referring clinicians, and patients to understand their workflows; mapping the entire patient journey from study setup through enrollment and retention; designing pre-screening workflows that are explainable and trustworthy; collaborating with design and engineering to translate clinical needs into crisp requirements and releases; building compliance and trust into the product by partnering with clinical, legal, privacy, and security teams; making pragmatic integration decisions across EHR/EMR, CTMS, EDC, and other systems; and defining outcome metrics that matter (time to match, referral completeness, site response time, enrollment conversion, retention).
You should have 5+ years of product experience in regulated or clinically complex environments. The company values shipping over process, moving fast within constraints, and a romantic belief that great software transforms people's work. You'll run discovery yourself, analyze data, write requirements, test workflows, and unblock delivery. A hands-on approach, confidence in your thinking paired with openness to being wrong, and a passion for AI (demonstrated through building or prototyping) are essential. The role requires someone who defaults to building, treats safety and data integrity as interesting constraints rather than obstacles, and can read a regulated clinical workflow to find the constraint that matters most.
