IVEN CHAVEZ

Key Skills

Software

Labelbox

Top Subject Matter

Speech Recognition

Top Data Types

Image

Audio

Top Label Types

Bounding Box

Point Key Point

Classification

Tracking

Transcription

Freelancer Overview

E-commerce Product Listing Designer. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution.

Labeling Experience

Computer Code Annotation and Evaluation for AI Programming Model Training

Computer Code ProgrammingComputer Programming Coding

Contributed to AI training projects focused on improving code generation and programming assistance models. Responsible for annotating and reviewing programming datasets used to train machine learning systems capable of understanding and generating code. Tasks included evaluating AI-generated code, labeling programming tasks, classifying code functionality, and writing prompt–response examples for supervised fine-tuning of coding models. Reviewed code snippets written in languages such as Python, JavaScript, and SQL, ensuring accuracy, logical correctness, and adherence to coding best practices. Assisted in rating the quality of AI-generated code based on correctness, efficiency, readability, and functionality. Followed detailed project guidelines to ensure consistency across training datasets used for AI coding assistants and automated programming tools. Worked with structured coding datasets and performed quality checks to ensure high-quality annotations used in training and evaluating advanced code generation models.

Contributed to AI training projects focused on improving code generation and programming assistance models. Responsible for annotating and reviewing programming datasets used to train machine learning systems capable of understanding and generating code. Tasks included evaluating AI-generated code, labeling programming tasks, classifying code functionality, and writing prompt–response examples for supervised fine-tuning of coding models. Reviewed code snippets written in languages such as Python, JavaScript, and SQL, ensuring accuracy, logical correctness, and adherence to coding best practices. Assisted in rating the quality of AI-generated code based on correctness, efficiency, readability, and functionality. Followed detailed project guidelines to ensure consistency across training datasets used for AI coding assistants and automated programming tools. Worked with structured coding datasets and performed quality checks to ensure high-quality annotations used in training and evaluating advanced code generation models.

2020 - Present

Advanced Video Annotation & Multi-Object Tracking for computer Vision

LabelboxVideoBounding BoxPoint Key Point

Executed large-scale video annotation projectsnputer vision and action recognition models Performed frame-by-frame labeling of dynamic scenes using bounding boxes, polygons, and keypoints while maintaining persistent object IDs for multi-object tracking tasks. Handled complex scenarios including occlusions, motion blur, dense traffic scenes, and rapid object movement Applied strict ontology rules and temporal consistency checks to ensure annotations met production-level Al training standards. Processed thousands of video frames across diverse environments, preparing datasets optimized for YOLO-based object detection and tracking pipelines. Maintained 95% annotation accuracy through structured QA workflows, guideline adherence, and systematic self-review. Collaborated with remote Al teams to resolve edge cases, improve labeling taxonomies, and deliver high quality datasets within tight deadlines.

Executed large-scale video annotation projectsnputer vision and action recognition models Performed frame-by-frame labeling of dynamic scenes using bounding boxes, polygons, and keypoints while maintaining persistent object IDs for multi-object tracking tasks. Handled complex scenarios including occlusions, motion blur, dense traffic scenes, and rapid object movement Applied strict ontology rules and temporal consistency checks to ensure annotations met production-level Al training standards. Processed thousands of video frames across diverse environments, preparing datasets optimized for YOLO-based object detection and tracking pipelines. Maintained 95% annotation accuracy through structured QA workflows, guideline adherence, and systematic self-review. Collaborated with remote Al teams to resolve edge cases, improve labeling taxonomies, and deliver high quality datasets within tight deadlines.

2023 - Present

Medical Imaging Annotation for DICOM-Based AI Diagnostics

Medical DicomPolygon

Worked on medical imaging annotation projects focused on training AI models for healthcare diagnostics using DICOM (Digital Imaging and Communications in Medicine) image datasets. Responsible for labeling and analyzing medical scans such as CT scans, MRI images, and X-ray images to support the development of machine learning models used for automated diagnostic assistance. Performed tasks including region segmentation, polygon annotation, classification of anatomical structures, and key-point labeling to identify important medical features within the scans. Carefully followed detailed medical annotation guidelines to ensure accurate and consistent labeling across large datasets. Used annotation tools compatible with DICOM image formats to review and label imaging data while maintaining strict data handling procedures. Collaborated with project teams to ensure annotation quality and improve datasets used for training AI-powered medical imaging systems. Maintained high levels of precision when identifying relevant structures and patterns within medical images to support the development of AI models for clinical research and diagnostic assistance.

Worked on medical imaging annotation projects focused on training AI models for healthcare diagnostics using DICOM (Digital Imaging and Communications in Medicine) image datasets. Responsible for labeling and analyzing medical scans such as CT scans, MRI images, and X-ray images to support the development of machine learning models used for automated diagnostic assistance. Performed tasks including region segmentation, polygon annotation, classification of anatomical structures, and key-point labeling to identify important medical features within the scans. Carefully followed detailed medical annotation guidelines to ensure accurate and consistent labeling across large datasets. Used annotation tools compatible with DICOM image formats to review and label imaging data while maintaining strict data handling procedures. Collaborated with project teams to ensure annotation quality and improve datasets used for training AI-powered medical imaging systems. Maintained high levels of precision when identifying relevant structures and patterns within medical images to support the development of AI models for clinical research and diagnostic assistance.

2023 - 2025

Text Annotation and Prompt–Response Evaluation for AI Model Training

TextClassification

Contributed to AI training projects focused on improving large language models and conversational AI systems by annotating and evaluating large text datasets. Responsible for labeling text data, classifying content categories, and reviewing prompt–response pairs to improve model accuracy and response quality. Performed text classification, prompt and response writing (SFT), response evaluation, and quality rating tasks according to project guidelines. Analyzed responses generated by AI systems and evaluated them for accuracy, relevance, safety, and linguistic quality. Assisted in generating high-quality prompt–response examples used for supervised fine-tuning of language models. Followed strict annotation guidelines and contributed to building structured datasets used for training and evaluating advanced AI systems.

Contributed to AI training projects focused on improving large language models and conversational AI systems by annotating and evaluating large text datasets. Responsible for labeling text data, classifying content categories, and reviewing prompt–response pairs to improve model accuracy and response quality. Performed text classification, prompt and response writing (SFT), response evaluation, and quality rating tasks according to project guidelines. Analyzed responses generated by AI systems and evaluated them for accuracy, relevance, safety, and linguistic quality. Assisted in generating high-quality prompt–response examples used for supervised fine-tuning of language models. Followed strict annotation guidelines and contributed to building structured datasets used for training and evaluating advanced AI systems.

2022 - 2025

Video Annotation for Object Detection and Motion Tracking

LabelboxVideoBounding BoxPoint Key Point

Worked on a large-scale video annotation project for computer vision and AI model training. Responsible for labeling and analyzing thousands of video frames to support the development of object detection, motion tracking, and action recognition models. Performed frame-by-frame video annotation, applying bounding boxes to track objects such as vehicles, pedestrians, and moving objects across sequences. Ensured temporal consistency while performing multi-frame object tracking and action classification across complex scenes. Used annotation tools such as CVAT, Labelbox, and YOLO-compatible labeling frameworks to produce high-quality training datasets for machine learning pipelines. Followed strict project guidelines and labeling taxonomies to maintain consistency across large datasets. Collaborated with project teams to review annotations and improve labeling quality for computer vision systems used in video analytics and autonomous technologies.

Worked on a large-scale video annotation project for computer vision and AI model training. Responsible for labeling and analyzing thousands of video frames to support the development of object detection, motion tracking, and action recognition models. Performed frame-by-frame video annotation, applying bounding boxes to track objects such as vehicles, pedestrians, and moving objects across sequences. Ensured temporal consistency while performing multi-frame object tracking and action classification across complex scenes. Used annotation tools such as CVAT, Labelbox, and YOLO-compatible labeling frameworks to produce high-quality training datasets for machine learning pipelines. Followed strict project guidelines and labeling taxonomies to maintain consistency across large datasets. Collaborated with project teams to review annotations and improve labeling quality for computer vision systems used in video analytics and autonomous technologies.

2022 - 2025

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Education

Work History