Emerson Rueda

Key Skills

Software

Labelbox

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Text

Top Task Types

Classification

Computer Programming Coding

Object Detection

Freelancer Overview

I’ve worked extensively as an AI Training Data Specialist across platforms like Scale AI, Mercor, CloudFactory, LabelBox, and Mindrift, where I focused on elevating model quality through precise data evaluation and annotation. My work spans code review, bug detection, pull-request assessment, and multi-modal classification (text, audio, and video). I consistently applied strict guidelines, performed logical and factual verifications, and provided high-quality corrections that directly shaped model behavior and reliability. Beyond annotation, I bring a strong technical foundation as a Full-Stack Developer, enabling me to understand model outputs from both a linguistic and engineering lens. This sets me apart: I don’t just label data—I understand how that data influences model performance, prompting, reasoning, and downstream accuracy. My background in software development, combined with hands-on experience in human-AI alignment, allows me to deliver precise, consistent, and scalable training data that measurably improves AI quality.

Labeling Experience

Multimodal Classification & Annotation (Audio, Text, Image, Video)

LabelboxImageEntity Ner ClassificationSegmentation

I performed end-to-end labeling and classification across multimodal datasets, including audio transcription and tagging, text categorization, image annotation, and video event recognition. Tasks included entity extraction, frame-level tagging, segmentation, object tracking, sentiment/emotion labeling, and multilingual content classification. I ensured high-consistency outputs by following detailed annotation guidelines, running QA validations, and maintaining accuracy targets throughout large-scale annotation batches. The project supported training pipelines for CV, ASR, and LLM models across diverse real-world scenarios.

I performed end-to-end labeling and classification across multimodal datasets, including audio transcription and tagging, text categorization, image annotation, and video event recognition. Tasks included entity extraction, frame-level tagging, segmentation, object tracking, sentiment/emotion labeling, and multilingual content classification. I ensured high-consistency outputs by following detailed annotation guidelines, running QA validations, and maintaining accuracy targets throughout large-scale annotation batches. The project supported training pipelines for CV, ASR, and LLM models across diverse real-world scenarios.

2024 - 2024

Code Review, Evaluation & LLM Technical Alignment

Scale AIComputer Code ProgrammingRLHFFine Tuning

I reviewed and evaluated AI-generated code, technical explanations, and developer-oriented outputs to ensure correctness, security, and alignment with project guidelines. Tasks included debugging, rewriting, and validating code across multiple languages, assessing pull requests, generating improved technical responses, and rating model behavior for accuracy, coherence, and reasoning quality. I performed granular evaluations of logic, complexity, and edge cases, applying strict quality rubrics to improve reliability for real-world developer workflows. Project involved thousands of samples with iterative feedback cycles and high-precision QA standards to ensure consistent, safe, and high-quality model performance.

I reviewed and evaluated AI-generated code, technical explanations, and developer-oriented outputs to ensure correctness, security, and alignment with project guidelines. Tasks included debugging, rewriting, and validating code across multiple languages, assessing pull requests, generating improved technical responses, and rating model behavior for accuracy, coherence, and reasoning quality. I performed granular evaluations of logic, complexity, and edge cases, applying strict quality rubrics to improve reliability for real-world developer workflows. Project involved thousands of samples with iterative feedback cycles and high-precision QA standards to ensure consistent, safe, and high-quality model performance.

2024 - 2024

Education

Work History