Moore A.

Key Skills

Software

Snorkel AI

Labelbox

Encord

SuperAnnotate

CVAT

Top Subject Matter

Machine learning evaluation and reinforcement learning from human feedback (RLHF) for annotation quality

LLM/vision output generation and qualitative AI output evaluation using self-guided RLHF

Top Data Types

Text

Image

Document

Top Task Types

Prompt + Response Writing (SFT)

Segmentation

RLHF

Evaluation/Rating

Transcription

Object Detection

Classification

Freelancer Overview

Senior Data Annotator at Azumo (04/2025 - Present): human-in-the-loop evaluation, RLHF protocol refinement, and integrat. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Snorkel AI and Labelbox. Education includes Bachelor of Engineering, Federal University of Technology Minna (2026). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Prompt + Response Writing (SFT).

Labeling Experience

Senior Data Annotator at Azumo (04/2025 - Present): human-in-the-loop evaluation, RLHF protocol refinement, and integration of Snorkel-based programmatic labeling

Snorkel AIText

Led human-in-the-loop evaluation for 10,000+ technical and qualitative data points using structured, logic-based guidelines to achieve 98% accuracy. Partnered with an AI research team to guide training of multiple machine learning models using domain analysis for 97% precision and recall. Delivered RLHF-focused assessment and refinement of annotation protocols to handle edge cases and speed up the evaluation workflow. • Applied complex guidelines for consistent evaluations across large datasets • Supported training of 5 machine learning models through high-quality annotations • Performed RLHF activities to improve protocol robustness and edge-case coverage • Reduced processing and evaluation time by 30% using programmatic labeling (Snorkel) while maintaining high-fidelity outputs

2025 - Present

Senior Data Annotator at IIAR (11/2024 - 12/2025): large-scale annotation plus prompt-based AI output evaluation

LabelboxTextPrompt + Response Writing (SFT)

Labeled and annotated 50,000 data items to ensure high-quality output while improving annotation speed through iterative feedback. Built and refined complex prompts to generate precise, artifact-free visual outputs by applying LLM/vision model interpretation. Iteratively reviewed and evaluated AI outputs against qualitative requirements using self-guided RLHF and maintained consistent communication with data scientists. • Produced high-quality annotations across 50,000 items • Enhanced annotation workflows based on feedback to improve tool performance • Developed prompt strategies for artifact-free visual generation (LLM/vision) • Conducted self-guided RLHF review loops and collaborated with data scientists for iteration

2024 - 2025

Education

Work History