Data Engine & Annotation Systems Engineer

Simbe is building the AI powered operating system for physical retail. Our autonomous robots and multimodal computer vision platform turn complex, constantly changing stores into accurate, actionable intelligence for leading retailers around the world. Simbe combines robotics, computer vision, machine learning, data infrastructure, and customer focused product design to help retailers improve shelf availability, price and promo execution, inventory accuracy, and store team productivity.

Simbe is looking for a Data Engine & Annotation Systems Engineer to own the systems, workflows, and tooling that power high quality training data for our computer vision models. This role goes beyond annotation coordination. You will help build the data engine behind Simbe's AI platform: model assisted labeling, data quality checks, annotation guidelines, error mining, dataset versioning, active learning, and evaluation workflows that improve model performance and accelerate customer value.

Why This Role Is High Impact

• You will help create the feedback loop that makes Simbe's AI systems better every week.
• You will improve the quality, speed, and reliability of data used to train and evaluate production models.
• You will work across human annotation, automation, model outputs, QA, and customer impact.

Responsibilities

• Own annotation systems and workflows. Oversee and improve image and video annotation workflows, including task setup, guideline creation, quality control, edge case handling, and throughput monitoring.
• Build data engine tooling. Develop Python, web, and automation tools that make it easier to request annotations, review results, clean data, export datasets, and evaluate model performance.
• Improve data quality. Design checks that identify inconsistent labels, oversized or undersized boxes, missing annotations, duplicate data, class imbalance, and other issues that can degrade model performance.
• Integrate model assisted workflows. Evaluate and integrate auto annotation, pre labeling, active learning, hard case mining, and model in the loop review systems to improve annotation efficiency.
• Support dataset versioning and evaluation. Partner with CV engineers to maintain reliable datasets, evaluation splits, benchmark views, and release readiness workflows.
• Coordinate across teams. Work with annotation teams, Computer Vision, Product, Customer Success, and Data teams to make sure annotation systems support current customer priorities and future product needs.
• Measure and improve performance. Track annotation quality, turnaround time, capacity, cost, rework rates, and model impact to improve the operating system for data creation.