Data Engine & Annotation Systems Engineer

Simbe Robotics•San Francisco Bay Area, CA

About The Position

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.

Requirements

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.

Nice To Haves

Experience with CVAT, FiftyOne, Labelbox, Scale, Roboflow, Supervisely, or similar annotation and dataset tools.
Experience with image, video, robotics, retail, autonomous vehicle, industrial inspection, or sensor data annotation.
Experience with active learning, auto labeling, synthetic data, evaluation dashboards, or dataset versioning.
Experience with object detection, segmentation, OCR, barcode localization, or other computer vision workflows.
Experience managing external annotation vendors or distributed annotation teams.

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.