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Matias Rodrigo Gonzalez Balbi

Matias Rodrigo Gonzalez Balbi

AI Data Specialist — RLHF & Response Evaluation

Argentina flagBuenos Aires, Argentina
$20.00/hrIntermediateRemotasksInternal Proprietary Tooling

Key Skills

Software

RemotasksRemotasks
Internal/Proprietary Tooling

Top Subject Matter

LLM output evaluation
reinforcement learning
human feedback

Top Data Types

TextText
AudioAudio
VideoVideo

Top Task Types

RLHF
Transcription
Classification
Evaluation Rating
Object Detection
Data Collection

Freelancer Overview

AI Data Specialist — RLHF & Response Evaluation (Meta AI via Outlier / Multimango). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes University Analyst in Data Science, Universidad Nacional de Luján (2024) and Bachelor's Degree in Economic Sciences, University of Buenos Aires (2024). AI-training focus includes data types such as Text and labeling workflows including RLHF.

IntermediateEnglishSpanish

Labeling Experience

AI Data Specialist — RLHF & Response Evaluation (Meta AI via Outlier / Multimango)

TextRLHF
As an AI Data Specialist for RLHF and response evaluation, I conducted large-scale LLM output comparisons and preference rankings. My work focused on structured rubric-based assessments emphasizing helpfulness, accuracy, harmlessness, and instruction-following. I consistently maintained annotation quality over extensive daily sessions. • Performed comparative evaluation of LLM outputs using structured rubrics. • Conducted preference ranking for reinforcement learning from human feedback (RLHF). • Evaluated model-generated content employing multi-dimensional quality frameworks. • Maintained high annotation standards across 8–10 hour daily workloads.

As an AI Data Specialist for RLHF and response evaluation, I conducted large-scale LLM output comparisons and preference rankings. My work focused on structured rubric-based assessments emphasizing helpfulness, accuracy, harmlessness, and instruction-following. I consistently maintained annotation quality over extensive daily sessions. • Performed comparative evaluation of LLM outputs using structured rubrics. • Conducted preference ranking for reinforcement learning from human feedback (RLHF). • Evaluated model-generated content employing multi-dimensional quality frameworks. • Maintained high annotation standards across 8–10 hour daily workloads.

2025 - Present

Education

U

Universidad Nacional de Luján

University Analyst in Data Science, Data Science

University Analyst in Data Science
2024

Work History

M

MT Studies

Project & Operations Coordinator

London
2022 - Present
B

B&C Asset Management

Data Engineering Consultant

Buenos Aires
2024 - 2024