Deploying secure and reliable AI agents across healthcare, retail,…. Backed by Y Combinator.
About the role
As a starting point, we’ve created an autonomous loop that detects failures (real and in simulation), creates llm unit-tests to reproduce those failures, and iterates until those are solved without introducing regressions. This is rather effective at autonomously tuning the behavior of our agents without significant manual interference. We hope to improve and push the boundaries here significantly.
What they're looking for
- Any (new grads ok) of experience
- Will sponsor
- Full-time Engineering role
More about this role
Refactor is working to eliminate the $50B+ labor cost of healthcare reimbursement.
We’re building a configurable AI platform that works with existing electronic health record systems to enable autonomous billing for healthcare providers.
We are extremely fortunate to be working with enterprise billing companies handling $4B+ in medical claims across hundreds of clinics along with some of the largest health systems in the country ($10B+ in net patient revenue), which has given us a great position to attack and solve this critical problem for the largest and most important vertical in the world.
Healthcare reimbursement is incredibly complex due to the large number of insurance companies (and third-party administrators), electronic health record systems, and specialties, each with their own rules and workflows. This makes deployment and configuration to each of our tenants exceptionally cumbersome and time-consuming.
An immediate goal of ours is to create shared infrastructure and tooling to automate this (think of this like an AI FDE), which has proven to be a difficult and exciting technical challenge!
As a starting point, we’ve created an autonomous loop that detects failures...
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