AI Vision Data Engine & Annotation Engineer

Simbe-Robotics; San Francisco (CA); Sur place; USD 120 000 - 155 000; Plein temps

Résumé du poste

Simbe seeks a Data Engine & Annotation Systems Engineer to own the data engine powering training for our computer vision models. You will lead annotation systems, data quality checks, active learning, and dataset versioning, while collaborating with CV, product, and customer success teams.

You will design scalable tooling in Python and automation, integrate model-assisted workflows, and help ensure high-quality data for production systems.

Qualifications

• 3+ years in software engineering, data tooling, or ML data ops.
• Proven Python scripting and data workflow development.
• Experience with production debugging and Linux environments.
• Experience building internal tools and front-end components for internal use.
• Strong ability to write clear annotation guidelines and docs.
• Deep understanding of data quality and its impact on models.
• Ability to coordinate across technical and operations teams while improving systems.

Responsabilités

• Own and improve image/video annotation workflows and guidelines.
• Build data engine tooling to request, review, clean, export, and evaluate datasets.
• Design checks for labeling quality, edge cases, class imbalance, and data issues.
• Evaluate and integrate auto-annotation, active learning, and model-in-the-loop systems.
• Support dataset versioning, evaluation splits, and release readiness workflows.
• Coordinate with annotation teams, CV, product, customer success, and data teams.
• Track metrics like quality, turnaround time, capacity, and cost to improve data creation.

Connaissances

Python

Bash/Linux

Frontend development

Documentation

Cross-team coordination

Data QA

APIs

Outils

CVAT

FiftyOne

Labelbox

Scale

Roboflow

Supervisely

Description du poste

Simbe seeks a Data Engine & Annotation Systems Engineer to own the data engine powering training for our computer vision models. You will lead annotation systems, data quality checks, active learning, and dataset versioning, while collaborating with CV, product, and customer success teams.

You will design scalable tooling in Python and automation, integrate model-assisted workflows, and help ensure high-quality data for production systems.