IVEN CHAVEZ

Key Skills

Software

Labelbox

Roboflow

Snorkel AI

Scale AI

CVAT

AWS SageMaker

Other

Top Subject Matter

AI Evaluation & Safety

Biology Domain Expertise

AI Training

Top Data Types

Image

Audio

Text

Video

Document

Top Task Types

Bounding Box

Point/Key Point

Classification

Tracking

Transcription

Polygon

RLHF

Entity (NER) Classification

Text Generation

Red Teaming

Prompt + Response Writing (SFT)

Question Answering

Data Collection

Freelancer Overview

LLM Evaluation & AI Safety Specialist. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Prompt + Response Writing (SFT).

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

CS/Software Engineering Problem Solving & Evaluation Specialist

Snorkel AI

Independently developed, reviewed, and adjudicated complex computer science and software engineering solutions with an emphasis on algorithms and coding challenges. Managed high-volume datasets and contributed structured feedback for AI training pipelines supporting STEM problem-solving models. Utilized Snorkel AI-style tools and multiple programming languages to author and evaluate production-ready code. • Code review and adjudication to ensure correctness, clarity, and efficiency. • Worked asynchronously up to 40 hours per week on CS/SE evaluation tasks. • Maintained proper version control with Git/GitHub and debugging using Jupyter Notebook. • Ensured alignment with engineering best practices in all labeling and evaluation tasks.

Independently developed, reviewed, and adjudicated complex computer science and software engineering solutions with an emphasis on algorithms and coding challenges. Managed high-volume datasets and contributed structured feedback for AI training pipelines supporting STEM problem-solving models. Utilized Snorkel AI-style tools and multiple programming languages to author and evaluate production-ready code. • Code review and adjudication to ensure correctness, clarity, and efficiency. • Worked asynchronously up to 40 hours per week on CS/SE evaluation tasks. • Maintained proper version control with Git/GitHub and debugging using Jupyter Notebook. • Ensured alignment with engineering best practices in all labeling and evaluation tasks.

2024 - 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

AI Training Specialist

OtherTextRLHFPrompt Response Writing SFT

Conducted Reinforcement Learning from Human Feedback (RLHF) and AI safety assessments on AI-generated text data in English and Spanish. Performed prompt–response evaluation, NLP tagging, and dataset categorization to improve large language model safety and alignment. Leveraged annotation and evaluation platforms for structured feedback and quality assurance. • Evaluated safety, factual consistency, and bias in model outputs. • Participated in multi-language evaluation for greater model robustness. • Designed systematic approaches for prompt and response quality validation. • Used software platforms to efficiently scale annotation processes.

Conducted Reinforcement Learning from Human Feedback (RLHF) and AI safety assessments on AI-generated text data in English and Spanish. Performed prompt–response evaluation, NLP tagging, and dataset categorization to improve large language model safety and alignment. Leveraged annotation and evaluation platforms for structured feedback and quality assurance. • Evaluated safety, factual consistency, and bias in model outputs. • Participated in multi-language evaluation for greater model robustness. • Designed systematic approaches for prompt and response quality validation. • Used software platforms to efficiently scale annotation processes.

2022 - Present

Text Annotation and Prompt–Response Evaluation for AI Model Training

CVATTextClassificationEntity Ner Classification

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 - Present

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Education

Work History