Anderson Chrispin

Key Skills

Software

Data Annotation Tech

Labelbox

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Text

Top Task Types

Audio Recording

Bounding Box

Computer Programming Coding

Evaluation Rating

Question Answering

Freelancer Overview

I have experience working on AI training and data annotation projects through platforms such as DataAnnotation, Outlier, and Aligner. My work has involved labeling and reviewing data rows to train large language models. I have performed tasks including QA evaluation, prompt engineering and rubrics engineering. I am comfortable following detailed guidelines, adapting to evolving rubrics, and maintaining high accuracy and consistency across large volumes of data. What sets me apart is my strong analytical mindset, attention to detail, and ability to interpret nuanced instructions. I have experience identifying edge cases, flagging ambiguous data, and providing structured feedback to improve model outputs. My background enables me to balance speed with precision while ensuring compliance with project standards. I am also experienced in calibration tasks and peer review, which has strengthened my ability to deliver reliable, high-quality annotations aligned with training objectives.

Labeling Experience

Rubric project

Scale AITextQuestion AnsweringEvaluation Rating

Contributors assessed prompt-response pairs for accuracy, reasoning quality, clarity, and adherence to complex guidelines. In addition to applying evaluation criteria, contributors were responsible for developing and refining detailed rubrics used to systematically assess model outputs. The project required identifying factual errors, logical gaps, and instruction-following issues, while providing comparative rankings and actionable feedback to enhance overall model performance.

Contributors assessed prompt-response pairs for accuracy, reasoning quality, clarity, and adherence to complex guidelines. In addition to applying evaluation criteria, contributors were responsible for developing and refining detailed rubrics used to systematically assess model outputs. The project required identifying factual errors, logical gaps, and instruction-following issues, while providing comparative rankings and actionable feedback to enhance overall model performance.

2025 - 2025

Education

Work History