HealthLeap
Tech / AI / Software
FullStackEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Full Stack Engineer at HealthLeap. Skills: React, TypeScript, APIs, complex data transformations, messy real-world data, relational databases, feature flags at scale. Own features end-to-end - from UI to API to database. Build complex feature flag systems for multi-tenant hospital deployments”
What You'll Achieve.
Build the product clinicians rely on every morning; shows them which patients need attention, surfaces the right clinical data, and helps them act fast; systems that real users depend on; something you shipped actually matters; own outcomes, not tasks
Industry & Context.
hard problems; messy data; complex permissions; systems that need to work every time
Startup unpredictability feels like chaos to you. We find it exciting., You need detailed specs before you start. We figure it out as we go., You've never owned something end-to-end. Here, you own outcomes, not tasks., You wait to be told what to do next. We need people who see what's needed and do it., You're looking for a 9-5 with predictable hours. We care about deep work and deep rest (we have a minimum leave policy), but when a hospital go-live is on the line, we show up - even if that means a 60+ hour week.
What They're Looking For.
Must Have
4+ years building production web applications, comfortable building APIs, Experience with complex data transformations and messy real-world data, Comfortable with relational databases, auth systems, and feature flags at scale, You've shipped products, not just features, You move fast and iterate based on what you learn
Nice to Have
Experience with LLMs or AI/ML pipelines, Background in healthcare or clinical software, Familiarity with Postgres, AWS, or containerized deployments
What You'll Do.
Own features end-to-end - from UI to API to database
Build complex feature flag systems for multi-tenant hospital deployments
Wrangle messy clinical data - turning unstructured text into structured
Work on the explainability problem: surfacing why a patient scored high
Help clinicians find in seconds what currently takes them hours buried in charts
Ship directly to production at hospitals like Cedars-Sinai
Full Job Description
ABOUT HEALTHLEAP HealthLeap builds AI that helps clinicians prioritize patients, surfaces the right data, and gets patients the care they need earlier, so they can leave the hospital sooner. We integrate with hospital electronic health record systems, screen 100% of patients daily, and risk-rank them in real time. Clinicians at Cedars-Sinai and Penn Medicine start every morning with HealthLeap — with Houston Methodist, Emory, and Intermountain Health deploying now. Real results: 39% more diagnoses. 4 days earlier detection. $11M/year ROI for our first site at Cedars Sinai. 7× revenue growth in 7 months. We started with malnutrition. We're expanding to every major condition to ensure no patient falls through the cracks. Sequoia and First Round are backing us to build the platform that screens every patient for everything and drives tangible outcomes. We're ~15 people. >$7M raised. SF-based, hybrid-friendly. Early enough to shape the product. Late enough to know it works. Results that are changing lives. ABOUT THE ROLE Build the product clinicians rely on every morning. HealthLeap's dashboard is the first thing dietitians open at Cedars-Sinai and Penn Medicine. It shows them which patients need attention, surfaces the right clinical data, and helps them act fast. You'll build the interfaces that make this possible - and the backend services that power them. WHY YOU You've built systems that real users depend on. You know what it feels like when something you shipped actually matters. You're energized by hard problems - messy data, complex permissions, systems that need to work every time. You've felt the chaos of a startup and you liked it. WHAT YOU'LL DO - Own features end-to-end - from UI to API to database - Build complex feature flag systems for multi-tenant hospital deployments - Wrangle messy clinical data - turning unstructured text into structured, usable information - Work on the explainability problem: surfacing why a patient scored high, not just the score
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