Senior Annotation and Data Pipeline Manager

Job description

Job responsibilities

• 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.

Job requirements

• 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.
• You can build, not just manage. Strong Python (Pandas, NumPy, 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.