Clinical AI Engineer

We’re Heidi. We're building the future of healthcare by giving every clinician the earth's finest AI Care Partner. Our platform has absorbed the administrative chaos of 175 million patient visits and supported 67 million clinical hours. Today, we support 2.8 million patient sessions a week across 190+ countries, in 110 languages and over 200 specialties. Healthcare systems are failing us; clinicians spend more time on documentation than on patients, and the human connection that makes medicine worth practicing is eroding. Our mission is simple: double the world’s capacity for care and strengthen the human connection at its heart. We found product-market fit with a freemium medical scribe that clinicians love. Now, we're expanding. Every task a clinician hands to Heidi is a patient who feels more attended to, a health system unclogged, and a clinician who gets to be a clinician again. We’ve grown annual recurring revenue from $1 million to $50 million in two years. To go further, we’ve secured US$340 million: a $100 million Series C led by Blackbird, with Phoenix Court, Point72 Private Investments and Headline, alongside a $240 million growth investment led by General Catalyst’s Customer Value Fund. If you want to join us in doing work worth shipping, jump in. The role We’re looking for a doctor who has moved into AI and can code, to join Clinical AI R&D as a Clinical AI Engineer. You’ll sit inside a product pod and own the evidence for its product: define the evaluation before anyone builds, run it to a standard a journal reviewer would accept, keep watching the product once it ships, publish what the evidence shows, and change the product when the evidence says to. The pod’s engineers can change the product but can’t judge whether a discharge summary missed something a consultant would catch. Clinicians can make that call but can’t open a pull request. You do both, and you treat the gap between a model’s output and good clinical practice as the thing you’re here to measure and close. The loop doesn’t end at launch. You stay with the product for its life in market.   What you’ll do Design the evaluation. Define evaluations for greenfield products to an academic standard: endpoints, comparators, ground truth, sample size, analysis plan. Set the bar before the result arrives. Build the measurement. Build, optimize and validate the LLM judges and rubrics that score the product at scale. Report their agreement with clinician ground truth, not their convenience, and retire the ones that drift. Run it and write it up. Execute the evaluat...

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