Senior Annotation and Data Pipeline Manager

The role

We have built a frontier model and put Eno in front of the world, fast. Behind that is a data engine: the machine that turns a raw human demonstration into data the model is measurably better for. This role owns that engine.

A worn glove and a camera produce a raw demonstration, not training data. You will build the pipeline and the annotation operation that turn raw demonstrations into clean, labeled, training-ready data, and make it scale with automation rather than headcount. You will own the datasets, what gets annotated, and the ontology, how it gets labeled, bring vision-language models to bear on trajectory labeling and language grounding, and close the loop so the engine keeps making the model better.

This role serves the whole operation, our own floors and our partner-funded collection.

• Run the data engine. Own the loop from raw trajectory and video to training-ready datasets, with validation steps that guarantee clean, correctly labeled data.

• Own datasets and ontology. Decide what gets annotated and how, designing the ontology with the model team for its training implications.

• Automate with models. Use vision-language models for automated trajectory annotation, language grounding, and data synthesis, so the pipeline scales without linear headcount, while holding the quality bar.

• Run the annotation operation. Stand up and scale labeling, internal and vendor, against a clear quality bar and a delivery schedule the model team can plan around.

• Close the loop. Turn real-robot eval failures into targeted collection and annotation jobs, and prove the new data improves the model.

• Own the metrics. Track inter-annotator agreement, label error rate, and throughput per annotator-hour, and drive them the right way.

• You have scaled an annotation or data pipeline at a serious operation. Four or more years in data or ML pipelines, including time leading the work. At a frontier AI lab or a top data operation, you have taken raw robot or embodied data to training-ready at volume and you know exactly where it breaks. The people who have done this are a small group.
  
  If you are one, we want to talk.

• You can build, not just manage. Strong Python (Pandas, Num Py, PyTorch) and SQL. You write the automation that shrinks the pipeline.

• ML literacy. You understand training versus test, precision and recall, and overfitting well enough to design an ontology that helps the model, not just labels data.

• Hands-on technical leadership. You can run a labeling operation and stay a hands-on contributor at the same time.

• Comfortable with ambiguity and speed. You move fast in a research-paced environment and bring order to it.