Job Description

About the Role
Global ML flood models now outperform traditional approaches on large rivers, but they still break down in the small, fast catchments where flash floods happen. Riverkin, a VC-backed ETH Zürich Spin-off, is closing that gap. We start from hydrological models trained across thousands of catchments worldwide and make them accurate locally, using a sensor network we deploy ourselves in exactly the places public datasets don't reach.

We are looking for an ML hydrologist to own that work. You will report to the CTO and work directly with our other hydrologist, who covers rainfall radar and short-term nowcasting.

Why this is interesting

Data nobody else has: Our own field-deployed sensors give you live streamflow observations in small, previously ungauged catchments.
An open problem: Whether large-sample models can be made reliable at flash-flood scale is