Amperos
Amperos Health is the AI-native insurance revenue recovery solution for outpatient providers. Our agentic solution recovers outstanding and denied claims across payer calls, portals, appeals, and resubmissions — escalating to your team only when needed. Customers see recovery rates up to 20% higher than traditional or manual processes, at a cost to collect up to 50% lower, within 4 weeks of onboarding. Our vision is a world where every provider has an AI-powered back office that handles insurance end-to-end — so their staff can spend less time chasing claims and more time on what healthcare is meant to be about: operating and growing their practice to serve more patients. Amperos is backed by leading investors, including Bessemer Venture Partners, Uncork Capital, Neo, and angels from OpenAI, Stripe, and more. To date, we've helped recover $700M+ in outstanding claims across 3,000 clinical locations in all 50 states. Interested in learning more? Set up time to chat with us here: https://www.amperos.com/request-a-demo
Open Positions at Amperos
4 open positions
Join Amperos Health as its first infrastructure engineer and lead the development of AWS infrastructure, DevOps, observability, security, compliance, and developer experience. This early-stage role offers significant ownership while supporting AI-powered healthcare revenue recovery products.
Build healthcare system integrations that combine APIs, RPA, and agentic workflows to automate revenue cycle management for providers. You’ll work directly with clients, improve integration reliability, and help shape a new team and repeatable integration framework.
Build full-stack and agentic AI products that help healthcare providers improve revenue cycle management. You’ll work directly with customers on voice AI, browser agents, LLM products, and end-to-end workflow solutions.
Build and improve AI agents that handle complex healthcare billing workflows, focusing on LLM orchestration, model quality, evaluation, and AI infrastructure. This research-focused role combines deep Python engineering with applied LLM experimentation and ambitious conversational AI problems.