Open to opportunities

AUGUST MIKE

@augustmike

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Computer vision image annotation specialist improving AI training data with precise bounding boxes and segmentations.

Zimbabwe

Message

What I'm looking for

I’m looking for a computer vision data annotation role where I can deliver precise, consistent labels, run strong quality assurance, and collaborate with data science teams to support reliable AI/ML training datasets.

I’m a focused image annotation expert with 4 years in computer vision data, specializing in bounding boxes, polygons, segmentations, and key point annotations for AI/ML model training.

In my ImageAnnotation Specialist role, I performed object detection, semantic segmentation, and key point labeling across datasets of 1350 images, keeping annotation accuracy high through multiple rounds of quality assurance and cross-check reviews.

I collaborate closely with ML engineers to align labeling guidelines with project goals, which helped me achieve annotation accuracy rates of 80% and reduce model training errors.

Most recently, as a Data Annotation Associate, I annotated diverse computer vision datasets for autonomous driving, healthcare imaging, and retail AI, followed strict labeling protocols, flagged ambiguous images, supported data cleaning and formatting, and helped improve labeling guidelines with project leads.

Experience

Work history, roles, and key accomplishments

Data Annotation Associate

Mindrift

Jan 2024 - Jan 2025 (1 year)

Annotated diverse computer vision datasets for autonomous driving, healthcare imaging, and retail AI by following strict labeling protocols to maintain consistency across large teams. Flagged ambiguous images, helped improve labeling guidelines with project leads, and supported data cleaning and formatting for ML pipeline integration.

Semantic Instance Segmentation Support Keypoint Landmark Labeling Data Cleaning ML Pipeline Data Formatting Team Consistency And QA

Image Annotation Specialist

Remo Task

Jan 2020 - Jan 2023 (3 years)

Performed object detection, semantic segmentation, and key point labeling across datasets of 1,350 images, ensuring accuracy through multiple QA rounds and cross-check reviews. Collaborated with ML engineers to align guidelines with project goals, achieving 80% annotation accuracy and reducing model training errors.

Polygon Instance Segmentation Keypoint Landmark Annotation Dataset Consistency Checks Labeling Tools (CVAT Labelbox VGG Image Annotator Roboflow)