ML Ops Engineer

Our ML teams build and optimize the models at the core of our autonomy stack. As a team scales, the workflow the models run on needs to move from a manual, organically grown setup onto a controlled, automated, and reproducible footing.

As an MLOps Engineer, operating out of Paris, Lausanne, or Zurich, you will own that machinery, across training and evaluation pipelines, CI, experiment tracking, and reproducibility, so that models are trained, benchmarked, and compared in a controlled and repeatable way. You free the modelers to focus on models rather than infrastructure, and you set the standard the team builds on.

• Training & Evaluation Pipelines: Build and maintain the reproducible training and evaluation pipelines the modelers run on, along with the pipeline templates and tooling they build on. The verdict on model quality stays with the modelers.