Data Engine & Annotation Systems Engineer, Simbe

• 3+ years of experience in software engineering, data tooling, ML data operations, annotation systems, data QA, or related technical work
• Strong Python skills, including experience building scripts, data workflows, APIs, or internal tools
• Comfort with Bash, Linux, Git, and production debugging workflows
• Experience with web frontend or full stack development for internal tools
• Strong communication skills and ability to create clear annotation guidelines, documentation, and process improvements
• High attention to detail and a strong understanding of how data quality affects model quality
• Ability to coordinate across technical and operational teams while still writing code and improving systems

Desirable

• 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
• 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
• 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