About the role
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 systems that screen every hospital patient, every day. HealthLeap processes billions of data points from hospital EHRs - labs, vitals, clinical notes, structured records - and turns them into real-time risk scores that clinicians rely on every morning. You'll build the backend systems and infrastructure that make this possible. Why you You've built systems that handle real scale - not toy projects, real data, real users, real uptime
requirements. You've debugged distributed systems at 2am. You've dealt with messy third-party APIs that had no documentation. You're the person who figures things out when the docs don't help.
What you'll do Build data pipelines that process millions of patient records daily Parse 50,000+ clinical notes per patient using LLMs - without hallucinating Own infrastructure on AWS and Kubernetes - deploy it, monitor it, fix it when it breaks Design systems for high concurrency and reliability - clinicians depend on this every morning Build feature engineering pipelines for ML models