Shawn Daniel

Key Skills

Software

Appen

Clickworker

CrowdSource

Mercor

Surge AI

Toloka

Top Subject Matter

Large Language Models and Natural Language Processing

Top Data Types

Text

Image

Video

Top Task Types

Text Summarization

Transcription

Object Detection

Question Answering

Text Generation

Evaluation Rating

Classification

Freelancer Overview

AI Trainer (Freelance). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Outlier AI and Appen. Education includes Bachelor of Arts, Thompson Rivers University (2025) and Diploma, Kenya Institute of Mass Communication (2024). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Training Contributor

AppenText

Conducted AI response evaluations and data annotation to enhance performance of machine learning models. Ensured quality and consistency of LLM outputs through guideline adherence and comprehensive reviews. Provided reliable feedback to directly support iterative AI refinement. • Reviewed outputs for linguistic accuracy and relevance • Maintained consistency across varying micro-tasks • Applied strict adherence to diverse project guidelines • Supported real-world performance improvements in AI systems

Conducted AI response evaluations and data annotation to enhance performance of machine learning models. Ensured quality and consistency of LLM outputs through guideline adherence and comprehensive reviews. Provided reliable feedback to directly support iterative AI refinement. • Reviewed outputs for linguistic accuracy and relevance • Maintained consistency across varying micro-tasks • Applied strict adherence to diverse project guidelines • Supported real-world performance improvements in AI systems

2025 - 2026

AI Trainer (Freelance)

Text

Developed prompts and problem sets to challenge and train large language models. Evaluated and ranked AI-generated responses using detailed rubrics tailored to criteria such as clarity, correctness, and reasoning. Provided comprehensive feedback to support continuous improvement of LLMs. • Created prompts targeting complex model weaknesses • Utilized structured rubrics for consistent evaluations • Assessed responses for clarity, depth, and factual accuracy • Reduced hallucinations and improved output quality

Developed prompts and problem sets to challenge and train large language models. Evaluated and ranked AI-generated responses using detailed rubrics tailored to criteria such as clarity, correctness, and reasoning. Provided comprehensive feedback to support continuous improvement of LLMs. • Created prompts targeting complex model weaknesses • Utilized structured rubrics for consistent evaluations • Assessed responses for clarity, depth, and factual accuracy • Reduced hallucinations and improved output quality

2025 - 2026

Education

Work History