Quexada R.

Key Skills

Software

Scale AI

CVAT

Supervisely

Appen

Labelbox

Other

Top Subject Matter

Autonomous Vehicles / Computer Vision

Retail Domain Expertise

Computer Vision

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Entity (NER) Classification

Freelancer Overview

Senior Image Annotation Specialist. Core strengths include Scale AI, CVAT, and Supervisely. Education includes Bachelor of Science, University of Texas at Dallas (2018) and Professional Certificate, Google (2021). AI-training focus includes data types such as Image, Text, and Geospatial and labeling workflows including Bounding Box, Entity (NER) Classification, and Evaluation.

Labeling Experience

Senior Image Annotation Specialist

Scale AIImageBounding Box

As a Senior Image Annotation Specialist at Scale AI, I annotated image and video data for autonomous vehicle projects, focusing on bounding box and polygon tasks. I achieved 99.2% bounding box tightness and implemented double-pass validation on LiDAR-projected frames. I also created resources and guided best practices for complex annotation workflows. • Labeled 1,500+ images weekly using bounding boxes and polygon segmentation • Improved accuracy through consensus workflows and validation tools • Employed CVAT and Supervisely platforms for annotation tasks • Developed visual guides and geometry quality control processes

2022 - Present

LLM Response Rater

OtherText

Recently, I completed evaluation and ranking of model responses for a large language model fine-tuning project. My work involved annotating 1,500 dialogue turns for RLHF and assessing gold label agreement. This effort contributed to quality assurance in LLM development. • Evaluated model responses for 1,500 dialogue turns during LLM training • Achieved 96% agreement with provided gold standard responses • Used RLHF evaluation protocols for prompt-response annotation work • Supported LLM fine-tuning and improvement through high-quality feedback

2023 - 2023

Data & Image Annotator (Text Labeling)

AppenTextEntity (NER) Classification

During my contract at Appen, I performed text annotation for generative AI and NLP projects. Tasks included intent classification, sentiment tagging, and entity (NER) classification for customer queries. This contributed to improved model fine-tuning and accuracy for client applications. • Annotated 5,000+ customer support queries for intent, entity, and sentiment labels • Utilized predefined style guides to ensure consistent text labeling • Leveraged Appen and Labelbox platforms for text data annotation • Helped support generative AI model advancements through quality data

2020 - 2021

Data & Image Annotator

AppenImageBounding Box

As a Data & Image Annotator at Appen, I labeled visual and textual datasets for commercial and AI fine-tuning projects. My work included bounding box annotation for retail object detection and text tagging for intent and sentiment classification. I maintained over 97% annotation accuracy across diverse labeling tasks. • Labeled 12,000+ retail images for object detection using bounding boxes and classification • Completed 5,000+ customer support queries for intent and sentiment tagging • Used tools including Labelbox and Appen for annotation • Exceeded weekly productivity targets while mastering multiple annotation style guides

2020 - 2021

Junior Data Curator

OtherBounding Box

As a Junior Data Curator at TACC, I cleaned and labeled satellite and drone-based geospatial imagery for climate research datasets. My labeling contributed to university research with a high level of inter-annotator agreement. I also optimized annotation workflows through scripting and documentation. • Labeled and curated geospatial images to prepare climate research data • Used custom and open-source annotation tools for image tasks • Developed documentation for handling edge cases and ambiguous labels • Improved quality assurance processes and reduced QA escalations

2018 - 2020

Education

Work History