Columbia, SC
Being one of the first engineers at a healthcare AI startup means doing a bit of everything — and that's exactly what I do. I spend time with MDS nurses, care coordinators, and clinical staff to understand how the product actually fits into their workflow. Those conversations directly shape what I build next, whether that's a new AI feature, a frontend interface that has to work under pressure, a backend service handling sensitive health data, or the DevOps infrastructure that keeps it all running. The flagship product is a document intelligence platform that reads clinical records and surfaces the findings that matter most for Medicare and Medicaid form completion — turning hours of manual review into something fast and dependable. I've shipped features across the entire stack: built the core AI pipeline, designed interfaces used by clinical staff daily, structured the architecture of new features, and am on call for production issues when they come up. Being a founding engineer means the product's quality is personal — there's no one else to blame, and no problem too small or too large to own.
What I took from it: What it actually means to make AI work for real users — not just building the model, but building everything around it so it's reliable, understandable, and genuinely useful in the hands of someone doing critical work.
