AI Data Engine & Annotation Systems Architect

Simbe; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

Comprehensive health plans

Culture of learning

Flexible time off

Job summary

Simbe is seeking a Data Engine & Annotation Systems Engineer to own the systems, workflows, and tooling powering high-quality training data for our computer vision models.

You will build the data engine behind Simbe’s AI platform, implementing 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.

Qualifications

• 3+ years of experience in software engineering, data tooling, ML data operations, annotation systems, data QA, or related technical work.
• Comfort with Bash, Linux, Git and production debugging workflows.
• Strong communication skills with ability to create clear annotation guidelines and documentation.
• Experience with web frontend or full stack development for internal tools.
• Experience with CVAT, FiftyOne, Labelbox, Scale, Roboflow, Supervisely, or similar tools.
• Experience with annotation/data quality workflows in CV/AI projects.
• Experience with object detection, segmentation, OCR, or related CV tasks.

Responsibilities

• Own annotation systems and workflows, including setup, guidelines, quality control, edge case handling, and throughput monitoring.
• Build data engine tooling using Python, web, and automation to request annotations, review results, clean data, export datasets, and evaluate model performance.
• Improve data quality by designing checks for label consistency, bounding box sizes, missing annotations, duplicates, class imbalance, and other quality issues.
• Integrate model-assisted workflows such as auto annotation, pre-labeling, active learning, and hard case mining to boost efficiency.
• Support dataset versioning and evaluation in collaboration with CV engineers for releases and benchmarks.
• Coordinate across annotation teams, CV, product, customer success, and data teams to align systems with customer priorities.
• Monitor performance metrics like quality, turnaround time, capacity, cost, rework, and model impact.

Skills

Python

Strong communication

CV/annotation domain

Active learning

Tools

Bash

Linux

Git

CVAT

FiftyOne

Labelbox

Scale

Roboflow

Supervisely

Job description

Simbe is seeking a Data Engine & Annotation Systems Engineer to own the systems, workflows, and tooling powering high-quality training data for our computer vision models.

You will build the data engine behind Simbe’s AI platform, implementing 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.