Edwin K.

Key Skills

Software

Other

Mindrift

Remotasks

Roboflow

Data Annotation Tech

Clickworker

CrowdFlower

CrowdSource

CloudFactory

CVAT

Datumbox

Top Subject Matter

Machine learning evaluation and AI training dataset quality (RLHF, preference ranking, hallucination auditing)

Data cleaning

labeling pipeline QA

Top Data Types

Text

Top Task Types

Fine-tuning

Computer Programming/Coding

Data Collection

Prompt + Response Writing (SFT)

Text Summarization

Freelancer Overview

Independent Projects & Portfolio — AI Training Data Evaluation/Annotation Specialist (RLHF Preference & Bias Auditing). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Openpyxl and Other. Education includes Bachelor of Science, Moi University (2025). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including Evaluation, Rating, and Computer Programming.

Labeling Experience

Independent Projects & Portfolio — Data Quality Specialist (Annotation/Validation Pipelines)

Other

Designed local scripts and pipelines to parse, scrub, and structure messy or unstructured text and tabular datasets for analysis readiness. Audited and evaluated automated Python code blocks to verify data frame transformations and statistical correctness. Produced visual quality tracking outputs to identify and categorize distribution errors and labeling anomalies. • Data sanitization and structuring pipelines • Data frame transformation verification via Pandas • Numerical validation via NumPy statistical calculations • Visual distribution and labeling anomaly tracking via Matplotlib

2024 - Present

Independent Projects & Portfolio — AI Training Data Evaluation/Annotation Specialist (RLHF Preference & Bias Auditing)

Text

Conducted prompt-response and preference/bias audits on complex outputs to score logic accuracy and compliance with explicit formatting instructions. Performed qualitative and quantitative comparisons of dataset outputs, including edge-case safety parameters, to surface annotation or evaluation failures. Used statistical error mapping to identify and catalog labeling anomalies for ML quality improvement. • RLHF preference and bias auditing • Logic accuracy scoring and edge-case safety checks • Compliance verification against formatting instructions • Distribution error and labeling anomaly identification

2024 - Present

Education

Work History