Early-stage biotech · AMR diagnostics

An early-stage biotech startup was using machine learning to read mass-spectrometry data and predict antibiotic resistance from it. I built the pipeline, from the raw spectra through to the error rates a clinical lab judges a test by, and wrapped it in a command-line tool that someone who has never set up a Python environment could run.

I also built the demo we took into hospital and HMO labs. A clinician could move a slider to their own rate of resistance and watch what that did to the numbers, alongside the system they already use. The feedback out of those demos shaped what came next.

The decision I am proudest of there is that I opened the codebase to people who do not write code. I encoded the domain workflow as skills an AI agent applies, and committed the agent’s configuration to the repository so it behaved the same way for everyone working in it. Then I sat with a biochemist who had never used version control, and with a data scientist new to this kind of tooling, until each of them was putting work of their own into the project.