Senior Backend Engineer, AI & Data Platform

CONUS (YC F26) is the AI knowledge layer for medical devices, connecting manufacturers with hospitals. We’re live in 20 European hospitals and are now expanding into the US.

We’re growing our engineering team across backend, AI infrastructure and product engineering.

Build the infrastructure behind reliable medical knowledge.

Medical device information lives across PDFs, product catalogs and manufacturer portals. Hospitals add their own protocols and workflows. CONUS brings these sources together and makes them accessible through structured browsing and AI-powered search.

We’re looking for a senior backend engineer to build the systems that make this work. You’ll work directly with our CTO, Alexander, on the APIs, data infrastructure and AI services behind CONUS and CONUS Intelligence.

This is a hands-on engineering role with responsibility from architecture through production. You should be comfortable improving existing systems, making technical decisions and taking a feature from an unclear problem to something users can rely on.

What you’ll build

• Core platform services. Develop APIs and data models for medical devices, hospital content, users and permissions across the app and our hospital and manufacturer admin panels.

• Content pipelines. Improve how we import, validate, structure and update information from documents and product portfolios while preserving its connection to the original source.

• AI infrastructure. Build and improve retrieval, tool integrations and answer evaluation for CONUS Intelligence. Make it easier to trace an answer back to the information behind it.

• Production reliability. Improve performance, testing, monitoring and deployments. Investigate failures and address their underlying causes.

• End-to-end features. Work with product and frontend engineering to turn customer requirements into maintainable software.

What we’re looking for

• Strong Python skills and experience building production backend systems, ideally with Django.

• Sound judgment around API design, relational data modeling, permissions and application architecture.

• Experience taking systems into production and being responsible for how they behave afterward.

• The ability to work independently, explain tradeoffs and simplify problems rather than add unnecessary infrastructure.

• A practical approach to AI coding tools. You use them to move faster and remain responsible for understanding, reviewing and testing the result.