Alexander Aguilar

Key Skills

Software

Mercor

OneForma

CloudFactory

Data Annotation Tech

Labelbox

Remotasks

Snorkel AI

Internal/Proprietary Tooling

Other

Top Subject Matter

Programming/Developer-focused Annotation

Full-stack Code Evaluation

Legal Services & Contract Review

Top Data Types

Text

Image

Computer Code Programming

Document

Top Task Types

Fine Tuning

Prompt Response Writing SFT

Classification

Bounding Box

Object Detection

Text Generation

Question Answering

Text Summarization

Computer Programming Coding

Data Collection

Evaluation Rating

Function Calling

Transcription

Freelancer Overview

JavaScript Developer — Handshake (Project: Helix). Core strengths include Other. AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Computer Programming and Coding.

Labeling Experience

Labeler — OneForma

OneformaTextClassification

As a Labeler at OneForma, I executed labeling tasks on structured and unstructured datasets using classification and tagging guidelines. I maintained high accuracy while meeting platform quality and production targets. My work supported structured data labeling for AI model training across multiple projects. • Labeled and classified various text datasets • Applied tagging guidelines for structured annotation • Met accuracy and throughput benchmarks • Supported text-based AI model training workflows

As a Labeler at OneForma, I executed labeling tasks on structured and unstructured datasets using classification and tagging guidelines. I maintained high accuracy while meeting platform quality and production targets. My work supported structured data labeling for AI model training across multiple projects. • Labeled and classified various text datasets • Applied tagging guidelines for structured annotation • Met accuracy and throughput benchmarks • Supported text-based AI model training workflows

2025 - Present

JavaScript Developer — Handshake

Other

As a JavaScript Developer at Handshake, I contributed to AI training pipelines by building and evaluating code-based tasks. My full-stack expertise was leveraged to produce and review code samples used in developer-focused annotation workflows. These efforts supported model learning and evaluation in coding-related contexts. • Built and assessed code tasks for AI training • Reviewed developer-focused annotation workflows • Supported coding challenge dataset creation • Ensured code quality for training efficiency

As a JavaScript Developer at Handshake, I contributed to AI training pipelines by building and evaluating code-based tasks. My full-stack expertise was leveraged to produce and review code samples used in developer-focused annotation workflows. These efforts supported model learning and evaluation in coding-related contexts. • Built and assessed code tasks for AI training • Reviewed developer-focused annotation workflows • Supported coding challenge dataset creation • Ensured code quality for training efficiency

2025 - Present

Writer — Mercor

MercorTextPrompt Response Writing SFT

As a Writer at Mercor, I wrote high-quality prompts and structured content for AI training datasets, contributing to model fine-tuning. My work helped diversify and improve data quality using prompt engineering principles. I developed domain-relevant, structured training data for large language models. • Crafted prompts to enhance dataset diversity • Applied prompt engineering to improve data depth • Authored human-written content for model training • Supported dataset creation for AI domain adaptation

As a Writer at Mercor, I wrote high-quality prompts and structured content for AI training datasets, contributing to model fine-tuning. My work helped diversify and improve data quality using prompt engineering principles. I developed domain-relevant, structured training data for large language models. • Crafted prompts to enhance dataset diversity • Applied prompt engineering to improve data depth • Authored human-written content for model training • Supported dataset creation for AI domain adaptation

2025 - Present

Labeler / Reviewer — Alignerr

TextFine Tuning

As a Labeler/Reviewer at Alignerr, I labeled and reviewed AI model outputs for supervised fine-tuning, focusing on response quality and adherence to guidelines. I evaluated agentic coding tasks for code correctness and efficiency in line with prompt requirements. Quality assurance feedback was provided to ensure annotation consistency across projects. • Adhered to annotation rubrics for dataset creation • Reviewed and evaluated AI-generated code outputs • Maintained labeling standards for quality assurance • Supported code and text-based AI model fine-tuning

As a Labeler/Reviewer at Alignerr, I labeled and reviewed AI model outputs for supervised fine-tuning, focusing on response quality and adherence to guidelines. I evaluated agentic coding tasks for code correctness and efficiency in line with prompt requirements. Quality assurance feedback was provided to ensure annotation consistency across projects. • Adhered to annotation rubrics for dataset creation • Reviewed and evaluated AI-generated code outputs • Maintained labeling standards for quality assurance • Supported code and text-based AI model fine-tuning

2025 - Present

AI Data Annotator & Evaluator — Outlier AI

Text

As an AI Data Annotator & Evaluator at Outlier AI, I performed multi-turn conversation evaluation and response scoring across several business-focused projects. My responsibilities included authoring detailed preference justifications and calibrating evaluation dimensions for quality assurance. I contributed to reinforcement learning from human feedback pipelines and evaluated AI-generated math prompts and responses. • Built and assessed conversation tasks to evaluate model performance • Authored structured evaluation justifications and feedback • Supported RLHF data pipelines through response ranking • Applied text formatting and rubric-based scoring methods

As an AI Data Annotator & Evaluator at Outlier AI, I performed multi-turn conversation evaluation and response scoring across several business-focused projects. My responsibilities included authoring detailed preference justifications and calibrating evaluation dimensions for quality assurance. I contributed to reinforcement learning from human feedback pipelines and evaluated AI-generated math prompts and responses. • Built and assessed conversation tasks to evaluate model performance • Authored structured evaluation justifications and feedback • Supported RLHF data pipelines through response ranking • Applied text formatting and rubric-based scoring methods

2025 - Present

Education

Work History