Samuel A.

Key Skills

Software

CVAT

Scale AI

Other

Labelbox

Top Subject Matter

Autonomous vehicles

Robotics Domain Expertise

computer vision datasets

Top Data Types

Image

Video

Audio

Text

Top Task Types

Object Detection

Segmentation

Emotion Recognition

Classification

Bounding Box

Freelancer Overview

AI Annotation Engineer | DataForce International (Image/Object Detection & QA). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include CVAT, Scale AI, and Other. Education includes Bachelor of Science, Federal University Wukari Taraba (FUW) (2024). AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Object Detection, Segmentation, and Emotion Recognition.

Labeling Experience

AI Data Annotation Engineer - Dataforce International

CVATAudioSegmentationClassification

You managed large-scale content labeling operations and ensured data accuracy across multiple modalities for AI and search use cases. You worked with annotation platforms and contributed to calibration, guideline-driven consistency, and quality controls. You collaborated with ML stakeholders to align labeled outputs with project objectives and maintain high acceptance rates. • Annotated images for object detection and semantic segmentation using CVAT and Scale AI. • Built annotation playbooks and edge-case decision trees to reduce escalations and improve onboarding. • Conducted calibration reviews and delivered feedback sessions to sustain accuracy above 96%. • Supported audio and text labeling workflows including diarization, emotion labeling, ASR transcript correction, and classification tasks.

2024 - Present

AI Annotation Engineer

LabelboxImageBounding BoxSegmentation

No description provided.

2024 - 2026

AI Annotation Engineer | DataForce International (Text Annotation)

OtherTextClassification

Performed high-volume text annotation tasks spanning document classification, toxicity detection, intent labeling, and relevance rating. Ensured labels were consistent with guidelines to support search ranking models and other NLP workflows. Maintained quality through structured QA/QC and review cycles. • Labeled documents for classification and intent categories. • Annotated toxicity labels for moderation-oriented NLP tasks. • Produced relevance ratings to support search ranking datasets. • Followed guideline development and edge-case handling practices.

2024 - 2026

AI Annotation Engineer | DataForce International (Audio Labeling)

OtherAudioEmotion Recognition

Contributed to audio annotation for voice AI applications including speaker diarization, emotion labeling, and ASR transcript correction. Applied labeling guidelines to produce consistent, usable annotations for downstream models. Performed quality-oriented review activities to keep labeled outputs aligned with project requirements. • Labeled speaker diarization outputs for voice AI datasets. • Tagged emotions and performed ASR transcript correction. • Ensured guideline adherence through QA/QC checks. • Supported iterative improvements based on feedback during reviews.

2024 - 2026

AI Annotation Engineer | DataForce International (Segmentation Labeling & QA)

Scale AIVideoSegmentation

Performed image/video-related labeling for semantic segmentation tasks within autonomous vehicle and robotics datasets. Maintained segmentation label consistency by applying playbooks and handling edge-case decisions. Reinforced quality through calibration reviews and structured annotator feedback. • Labeled visual data for segmentation using CVAT and Scale AI platforms. • Used annotation guideline development to standardize outputs. • Reduced escalations by 40% via edge case decision trees. • Maintained project-level accuracy above 96% through QA/QC.

2024 - 2026

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Education

Work History