About the team The Data Operations team owns the entire data lifecycle behind STARK's AI stack: collection, acquisition, generation, curation, and management. We run our own data-collection campaigns across Europe, evaluate new sensors and platforms, and build the internal data platform that turns raw recordings into ready-to-use datasets. Everything we produce feeds directly into the perception and autonomy systems deployed on STARK's platforms.

Your mission Data is the fuel of STARK’s AI stack — you will build the engine that makes it usable. You will own the software backbone of our data platform: metadata systems, ETL pipelines, data contracts, catalogs, databases, and internal tools that let engineers find, understand, validate, and reuse terabytes of multi-sensor field data in minutes, not days. You will treat data context as a product: structured, searchable, version-aware, documented, and traceable from raw recording to processed asset, annotation delivery, dataset, and downstream ML workflow. Today, much of this is manual, scattered, or implicit — your job is to automate it away, support labeling efforts with the right data tooling, and turn operational data into reliable systems.

Responsibilities - Desi

About the team The Data Operations team owns the entire data lifecycle behind STARK's AI stack: collection, acquisition, generation, curation, and management. We run our own data-collection campaigns across Europe, evaluate new sensors and platforms, and build the internal data platform that turns raw recordings into ready-to-use datasets. Everything we produce feeds directly into the perception and autonomy systems deployed on STARK's platforms.

Your mission Data is the fuel of STARK’s AI stack — you will build the engine that makes it usable. You will own the software backbone of our data platform: metadata systems, ETL pipelines, data contracts, catalogs, databases, and internal tools that let engineers find, understand, validate, and reuse terabytes of multi-sensor field data in minutes, not days. You will treat data context as a product: structured, searchable, version-aware, documented, and traceable from raw recording to processed asset, annotation delivery, dataset, and downstream ML workflow. Today, much of this is manual, scattered, or implicit — your job is to automate it away, support labeling efforts with the right data tooling, and turn operational data into reliable systems.

Responsibilities - Desi