Tominelvin A.

Key Skills

Software

Scale AI

Remotasks

Appen

Labelbox

Other

CVAT

Top Subject Matter

AI training data preparation (text analytics and conversational data)

Reinforcement Learning from Human Feedback (conversational AI training)

Vision dataset annotation (image labeling)

Top Data Types

Text

Image

Top Task Types

RLHF

Segmentation

Freelancer Overview

AI Data Labeler & Annotation Specialist (Freelance / Project-Based). Core strengths include Scale AI, Remotasks, and Appen. AI-training focus includes data types such as Text and Image and labeling workflows including Entity (NER), RLHF, and Segmentation.

Labeling Experience

Image Labeling (from Data Labeling Expertise — Deep Dive)

CVATImageSegmentation

Completed image labeling work including bounding boxes and segmentation-style annotations. Assigned image categories and labeled relevant visual regions to support model learning. Ensured annotations were suitable for training and evaluation of computer vision tasks. • Bounding box and region annotation • Segmentation-style labeling • Image classification/category tagging • Annotation quality review for vision datasets

Not specified

RLHF & Content Quality Evaluation (Project-based)

OtherTextRLHF

Participated in RLHF workflows by rating, ranking, and refining AI-generated responses to align model behavior with human values. Conducted content safety review and assessed responses for quality, bias, and accuracy. Produced evaluation signals intended to strengthen conversational AI training outcomes. • RLHF response rating and ranking • Alignment refinement with human values • Safety and bias evaluation • Accuracy and helpfulness assessment

Not specified

AI Data Labeler & Annotation Specialist (Freelance / Project-Based)

Scale AIText

Provided advanced text annotation for sentiment, intent, entities, and topics with strong attention to detail. Executed named entity recognition tasks and ensured labeled outputs were clean and well-structured for training. Supported model training pipelines by improving performance through higher-quality labeled data. • Sentiment and intent labeling • NER entity tagging • Topic/entity classification • Quality checks for label structure and consistency

Not specified

Education

Work History