Adam Elnaba

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Task Types

Entity Ner Classification

Freelancer Overview

I am a detail-oriented software engineer specializing in AI data operations, data labeling, and annotation for machine learning workflows. My experience includes reviewing and annotating over 2,000+ AI-generated code completions across Python, JavaScript, and SQL, providing structured feedback, and maintaining a 98% QA accuracy. I have contributed to improving model correctness and reducing hallucinations by curating high-quality training data and edge cases for supervised fine-tuning. My technical toolkit includes Python, SQL, MongoDB, Streamlit, and various machine learning frameworks, and I have built robust data pipelines and dashboards for real-time analytics. Notable projects include developing hybrid intrusion detection systems using deep learning, language detection models with NLP techniques, and automation workflows that leverage natural language prompts. I thrive in cross-functional teams and am passionate about delivering high-quality, reliable training data for AI systems.

Labeling Experience

Data Annotator

LabelboxComputer Code ProgrammingEntity Ner Classification

-Reviewed and annotated over 2,000+ AI-generated code completions across languages such as Python, JavaScript, and SQL to improve model accuracy and adherence to best practices. – Identified and corrected logic errors and inefficiencies in hundreds of code samples, contributing to a 15-20% improvement in model correctness on internal benchmarks. – Provided structured technical feedback on AI code outputs with a 98% QA accuracy rating, ensuring consistency and reliability in training data. – Collaborated with a team of engineers and trainers to evaluate 100+ coding tasks weekly, optimizing the model’s problem- solving capabilities across real-world scenarios. – Helped reduce model hallucinations in code generation tasks by curating high-quality examples and edge cases for supervised fine-tuning – Maintained a 98% QA accuracy across weekly evaluations of 100+ coding and ML tasks. – Created structured ML training data covering Python, JS, SQL, DS/ML pipelines, and algorithmic reasoning ta

-Reviewed and annotated over 2,000+ AI-generated code completions across languages such as Python, JavaScript, and SQL to improve model accuracy and adherence to best practices. – Identified and corrected logic errors and inefficiencies in hundreds of code samples, contributing to a 15-20% improvement in model correctness on internal benchmarks. – Provided structured technical feedback on AI code outputs with a 98% QA accuracy rating, ensuring consistency and reliability in training data. – Collaborated with a team of engineers and trainers to evaluate 100+ coding tasks weekly, optimizing the model’s problem- solving capabilities across real-world scenarios. – Helped reduce model hallucinations in code generation tasks by curating high-quality examples and edge cases for supervised fine-tuning – Maintained a 98% QA accuracy across weekly evaluations of 100+ coding and ML tasks. – Created structured ML training data covering Python, JS, SQL, DS/ML pipelines, and algorithmic reasoning ta

2024

Education

Work History