Carlos Ernesto Guadamuz

Key Skills

Software

Data Annotation Tech

Remotasks

Scale AI

Top Subject Matter

Coding / Software related prompt generation and evaluation.

Logical and mathematical reasoning LLM evalutation, annotation and correction.

Video entities labelling.

Top Data Types

Computer Code Programming

Text

Video

Top Task Types

Computer Programming Coding

Evaluation Rating

Question Answering

RLHF

Text Generation

Freelancer Overview

I am experienced in multiple RLHF workflows. Specialized in code, and mathematical/logical reasoning input data creation, annotation, categorization, and evaluation. I've worked professionally with multiple LLMs both as a trainer and as a user. As an early adopter, I am particularly perceptive to the technology's primary strengths and weaknesses. My primary focus is providing high-quality training data, using complex and specific examples to help models significantly improve.

Labeling Experience

Computer Programming prompt reviewer.

Data Annotation TechComputer Code ProgrammingEvaluation RatingComputer Programming Coding

Rated and reviewed the work of annotators. Made sure all reviewed work was consistent with the project's guidelines and quality standards. Rated the work based on quality, coherence, and creativity. Work by annotators should be double-checked by reviewers to ensure that the work is accurate and consistent and that all code is correct and complete.

Rated and reviewed the work of annotators. Made sure all reviewed work was consistent with the project's guidelines and quality standards. Rated the work based on quality, coherence, and creativity. Work by annotators should be double-checked by reviewers to ensure that the work is accurate and consistent and that all code is correct and complete.

2023

Computer Programming response evaluator

Data Annotation TechComputer Code ProgrammingRLHFComputer Programming Coding

Provide high-quality software prompts (questions, debugging, code completion, feature requests, etc.) and annotated model answers based on multiple parameters regarding their quality, such as adherence, correctness, and style. Prompts required a specific level of complexity and quality, with other constraints such as language and context. Prompts were reviewed and constantly measured for quality and usefulness.

Provide high-quality software prompts (questions, debugging, code completion, feature requests, etc.) and annotated model answers based on multiple parameters regarding their quality, such as adherence, correctness, and style. Prompts required a specific level of complexity and quality, with other constraints such as language and context. Prompts were reviewed and constantly measured for quality and usefulness.

2023

Education

Work History