Make research results and model releases traceable to the data and decisions that produced them. You will build the infrastructure for learning responsibly.
Not open yet: Pilot expansion
This work starts when that stage arrives, so there is no application to submit today and we will not pretend otherwise. What is written below is what the role is for and what would make somebody right for it, published early on purpose so you can decide whether it is worth watching.
The work
Version datasets, model artifacts, experiment configurations and evaluation results. Build provenance, quality checks and reproducible pipelines. Separate household data from shared research datasets, and coordinate deletion and retention behavior with privacy engineers.
The milestone
In your first 90 days, deliver a reproducible experiment pipeline with dataset lineage and release checks that block unapproved inputs.
Evidence
Bring experience with data systems, ML operations or scientific computing. Show how you have detected contamination, data drift or irreproducible results.
Evidence, not credentials. We are describing work you can point at, in whatever form it exists.
The exercise
Trace a model-quality regression after a dataset update and explain which artifacts are necessary to reproduce it.