Research Engineer, Preference Data

As an ML data engineer here, you'll build the flywheel itself: the system that turns professional design work into training-grade preference data, and training results back into a better product. You'll work beside the researchers consuming what you build, inside the product code where the signals are born, and against the warehouse where they land. Your customer sits one desk away, and you'll feel it within days when a dataset you shipped lets them ask a question nobody could ask before.

This is not a support role, and it is not offline ETL. The pipelines you design run through a live canvas that professional teams use every day, under enterprise agreements with some of the most protective brands in the world. Capturing more without breaking trust or performance is the craft. If you want to train models and not build the systems that feed them, this isn't the seat. If you believe the next advances get won in the data, it is.

We think a dataset is a product: it has users, versions, and a quality bar. A training result should reproduce from a dataset fingerprint months later, and "where did this example come from" should always have an answer. That standard is rare. Here, it's the job.

The capture surface: what the product records, designed with product engineers and shipped in product code.

The pipeline from canvas to warehouse to training set: clean, versioned, reproducible from a fingerprint.

Dataset contracts and lineage: every example traceable to its origin, every result replayable months later.

The boundary system: what's capturable under which enterprise agreements, consent and isolation designed in from the start, sanitized slices for research and work trials.

Collection instruments: when historical signal runs out, the mechanisms that gather explicit feedback designers actually want to use.

The honest encoding of ambiguity: unpicked is not disliked, abandoned is not rejected, and the data should say so.