Pedro H.

Key Skills

Software

Scale AI

Top Subject Matter

Computer vision perception model datasets

Research-focused AI model datasets (text/image/audio)

Top Data Types

Image

Text

Top Task Types

Classification

Freelancer Overview

Annotation Analyst, Scale AI (image/video perception labeling and QA). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, N, and A. Education includes Bachelor of Science, National Autonomous University of Mexico (2020). AI-training focus includes data types such as Image and Text and labeling workflows including Classification and Entity (NER).

Labeling Experience

Generalist Data Annotator, xAI (multi-modal annotation for research models)

Text

Performed generalist annotation across text, image, and audio for research-focused AI models under tight deadlines. Adapted quickly to evolving labeling schemas and documentation requirements to ensure consistent dataset iteration. Recorded ambiguous cases with clear examples and issue reports to improve label consistency and reduce future annotation confusion. • Annotated text, images, and audio according to current schemas • Updated practices as tools and schemas evolved • Documented ambiguous cases with examples and issue reports • Streamlined guidelines to accelerate dataset iteration cycles

2024 - 2025

Annotation Analyst, Scale AI (image/video perception labeling and QA)

Scale AIImageClassification

Led large-scale perception dataset annotation for images and video to support model training and evaluation. Produced and validated 100,000+ high-quality labels while maintaining QA rates above 90% through standardized guidelines and checklists. Reduced rework by 30% and increased labeling throughput by 25% using lightweight Python validation scripts and consistent schema documentation. • Developed standardized labeling guidelines for consistent outputs • Implemented QA checklists and Python validators to detect errors • Trained and mentored annotators to improve consistency and reduce onboarding time • Collaborated with ML engineers to refine label schemas and document edge cases

2021 - 2024

Education

Work History