Data Engine & Annotation Systems Engineer

Simberobotics; San Francisco (CA); On-site; USD 110,000 - 150,000; Full time

Job summary

Simbe is seeking a Data Engine & Annotation Systems Engineer to own the systems and workflows that power high quality training data for our computer vision models. You will build the data engine behind Simbe's AI platform, enabling model assisted labeling, data quality checks, and dataset versioning to accelerate customer value.

This role spans annotation operations, automation, model outputs, QA, and cross-functional collaboration with CV, Product, Customer Success, and Data teams to improve

Qualifications

• 3+ years building software tooling for ML data.
• Experience designing data annotation systems.
• Strong Python scripting and data workflow experience.
• Ability to document processes and guidelines clearly.
• Excellent collaboration across cross-functional teams.

Responsibilities

• Own and improve annotation systems and workflows.
• Build data engine tooling to request, review, clean, and export datasets.
• Design checks for data quality and labeling consistency.
• Evaluate integration of auto-annotation and active learning.
• Support dataset versioning, evaluation splits, and release readiness.
• Coordinate with CV, Product, Customer Success, and Data teams.
• Monitor performance metrics and optimize data creation processes.

Skills

Python

Bash & Linux

Full stack

Data QA

Team coordination

Tools

CVAT

FiftyOne

Labelbox

Scale

Roboflow

Supervisely

Job description

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.

• 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.

• 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.

• 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.

Simbe Values:

• Result Driven - We are customer centric and results driven. We strive to create immense value for our team, partners, customers, and investors.

• Empathetic - We are sensitive and mindful. We support each other in challenging times, both professionally and personally.

• Transparent - We value open communication internally, and with our partners and customers. We are receptive to feedback.

• Agile - We are eager to learn and adapt quickly to changes and customer needs.

• Innovative - We are bold and innovative, with an intense focus on product design, user experience, and customer value.

• Leaders - We strive for excellence. We are accountable, the best at what we do, and leaders in our field.