Senior Annotation and Data Pipeline Manager

Genesis • San Carlos, CA

11d

About The Position

Genesis is a full-stack general-purpose robot company aiming to make general-purpose robots a reality, freeing people for creativity and exploration. We have developed a frontier model and a robot named Eno, powered by our GENE foundation model. This role is crucial for managing the data engine that transforms raw human demonstrations into valuable training data for our models. The successful candidate will build and scale the pipeline and annotation operation, ensuring clean, labeled, training-ready data is produced efficiently. This involves owning datasets, defining the ontology, leveraging vision-language models for annotation, and closing the feedback loop to continuously improve the robot model. The role supports both internal operations and partner-funded data collection efforts.

Requirements

• Scaled an annotation or data pipeline at a serious operation.
• Four or more years in data or ML pipelines, including time leading the work.
• Experience at a frontier AI lab or top data operation, taking raw robot or embodied data to training-ready at volume.
• Ability to build, not just manage.
• Strong Python (Pandas, NumPy, PyTorch) and SQL skills.
• ML literacy, understanding training vs. test, precision and recall, and overfitting.
• Hands-on technical leadership, able to run a labeling operation and remain a hands-on contributor.
• Comfortable with ambiguity and speed, moving fast in a research-paced environment and bringing order to it.

Responsibilities

• Run the data engine, owning the loop from raw trajectory and video to training-ready datasets with validation steps.
• Own datasets and ontology, deciding what gets annotated and how, and designing the ontology with the model team.
• Automate with models, using vision-language models for automated trajectory annotation, language grounding, and data synthesis to scale the pipeline.
• Run the annotation operation, standing up and scaling labeling (internal and vendor) against quality and delivery schedules.
• Close the loop by turning real-robot evaluation failures into targeted collection and annotation jobs, and proving data improvements.
• Own the metrics, tracking inter-annotator agreement, label error rate, and throughput per annotator-hour, and driving them positively.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed