Oyetunde O.

Key Skills

Software

Label Studio

CVAT

Labelbox

V7 Labs

Supervisely

Scale AI

AWS SageMaker

SuperAnnotate

Don't disclose

Other

Top Subject Matter

Autonomous driving

medical imaging

and multimodal AI (vision + NLP)

Top Data Types

Image

Text

Document

Top Task Types

Segmentation

Entity (NER) Classification

Freelancer Overview

Senior AI Training Specialist & Data Annotation Lead — NeuroScale AI. Core strengths include Label Studio, CVAT, and Labelbox. Education includes Bachelor of Science, University of Texas at Austin (2017) and Machine Learning Specialization, Coursera (Stanford/Andrew Ng) (2022). AI-training focus includes data types such as Image, Text, and Document and labeling workflows including Segmentation and Entity (NER) Classification.

Labeling Experience

Senior AI Training Specialist & Data Annotation Lead — NeuroScale AI

Label StudioImageSegmentation

Designed and deployed scalable annotation workflows processing 50,000+ monthly annotations across computer vision, NLP, and multimodal AI projects. Executed semantic segmentation, instance segmentation, and pixel-/polygon-based labeling to prepare high-quality training datasets. Built quality control workflows and measurement of inter-annotator agreement to ensure consistent dataset labels. • Semantic segmentation on 15,000+ autonomous vehicle images using polygon annotations in CVAT and Supervisely • Instance segmentation on 8,000+ medical imaging datasets (CT/MRI) using specialized imaging tools • Created 25,000+ bounding box annotations for object detection across 12 classes and 20,000+ keypoint labels for pose estimation • Developed annotation guidelines and quality rubrics achieving 96% inter-annotator agreement across distributed teams

2022 - Present

Senior AI Data Labeling Specialist & QA Engineer — Lumina AI Research

Scale AITextEntity (NER) Classification

Managed annotation operations using labeling platforms and cloud tooling to produce training datasets for computer vision and NLP. Performed panoptic segmentation and advanced visual labeling while also supporting data labeling for multi-domain AI initiatives. Implemented automated quality control systems (calibration and feedback loops) to sustain high inter-annotator agreement and labeling throughput. • Panoptic segmentation on 10,000+ street scene images for autonomous driving datasets • 3D bounding box annotations for LiDAR point cloud data using Segments.ai and SuperAnnotate 3D features • Polyline annotation for lane detection on 12,000+ highway images achieving 98% precision and performed audio transcription/speaker diarization for 3,000+ hours • Built automated quality control achieving 96% inter-annotator agreement and improved throughput by 40% via caching and asynchronous workflows

2020 - 2021

AI Data Annotator & Computer Vision Specialist — IntelliCore Research Labs

LabelboxDocumentSegmentation

Completed large-volume computer vision annotation to generate training ground truth for downstream model evaluation and model training. Labeled bounding boxes, polygons, and semantic segmentation targets using multiple annotation platforms. Produced structured labeling feedback for model output assessment while maintaining guideline-driven self QA. • 20,000+ image annotations including bounding boxes, polygons, and semantic segmentation using Labelbox and RectLabel • Object detection annotation for retail product recognition across 15,000+ product images and 50+ categories • Facial landmark annotation (68-point) on 8,000+ images using Dlib annotation tools • OCR ground truth annotation for document understanding on 5,000+ document images

2019 - 2020

Machine Learning Data Specialist — DataDriven Innovations

TextEntity (NER) Classification

Prepared training datasets for supervised learning models through data collection, cleaning, and systematic labeling. Performed NLP data labeling tasks using annotation tools to support entity extraction and relation annotation for model training. Automated preprocessing and quality validation to reduce manual processing time while maintaining detailed logs for reproducibility. • Data labeling for 10,000+ NLP samples including entity extraction and relation annotation using Brat and WebAnno • Built Python automation scripts for preprocessing and quality validation reducing manual processing time by 35% • Documented annotation processes with detailed logs for 150+ projects to ensure reproducibility • Supported supervised model training data preparation via consistent labeling standards

2017 - 2019

Education