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Simbe Robotics is building an AI-powered operating system for physical retail, combining autonomous robots and multimodal computer vision to help retailers improve shelf availability, pricing execution, inventory accuracy, and store productivity.
You will own the systems, workflows, and tooling that power high-quality training data for Simbe's computer vision models. This is a technical leadership role focused on building the data infrastructure that underpins the company's AI platform.
Key responsibilities include:
- Designing and implementing model-assisted labeling systems to improve annotation efficiency
- Building data quality checks and validation pipelines to ensure training data integrity
- Developing annotation guidelines and error mining workflows to identify and fix labeling issues
- Implementing dataset versioning and lineage tracking for reproducibility
- Creating active learning systems to prioritize which data to label next
- Building evaluation workflows that measure model performance improvements
- Collaborating with ML engineers, computer vision researchers, and product teams to understand data needs
- Scaling data infrastructure to handle growing volumes of retail imagery and sensor data
You should have strong experience with data infrastructure, machine learning workflows, and building systems that bridge data engineering and ML. Experience with computer vision datasets, annotation platforms, or similar data-intensive ML systems is valuable. You'll work on problems that directly impact model quality and customer value delivery.
