Gideon N.

Key Skills

Software

Don't disclose

Scale AI

Top Subject Matter

LLM output evaluation and AI training feedback

AI code generation evaluation and technical content labeling

LLM training dataset annotation and evaluation

Top Data Types

Text

Audio

Document

Top Task Types

RLHF

Text Generation

Evaluation/Rating

Freelancer Overview

AI Training & Data Annotation Engineer | Handshake AI. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose, Internal, and Proprietary Tooling. Education includes Master of Science, Carnegie Mellon University (CMU) (2025) and Bachelor of Science, Kirinyaga 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

AI Training & Data Annotation Engineer | Handshake AI

Don't discloseText

Performed AI training data evaluation by reviewing AI-generated outputs for quality, accuracy, clarity, and user experience. Applied structured evaluation guidelines to produce consistent, nuanced feedback that informs model improvement and reinforcement learning workflows. Identified inconsistencies and logical weaknesses to flag systematic failure patterns for the training team.• Reviewed diverse AI responses across content types and task categories.• Assessed outputs for quality, accuracy, clarity, and UX alignment.• Collaborated on scoring criteria, quality standards, and evaluation best practices.• Maintained high accuracy and consistency across large volumes of evaluations.

2025 - Present

Full-Stack Software Developer | Outlier AI

Evaluated AI-generated code outputs by assessing correctness, efficiency, and adherence to best practices. Produced structured editorial feedback and used detailed rubrics to ensure consistent, defensible scoring on ambiguous outputs. Built and supported internal tools and dashboards to streamline annotation and model evaluation workflows.• Assessed code outputs for correctness and efficiency.• Checked adherence to best practices and technical standards.• Applied attention to detail to resolve ambiguity in responses.• Contributed to evaluation rubric and reviewer guideline improvements.

2024 - Present

AI Data Annotation Specialist | Scale AI

Scale AITextRLHF

Annotated and evaluated large-scale AI training datasets across text, code, and structured data modalities. Used detailed evaluation rubrics to assess outputs for factual accuracy, coherence, safety, and instruction-following quality. Supported LLM training pipelines by contributing high-quality labeled data that informed model fine-tuning and RLHF processes.• Labeled and evaluated datasets to improve model performance and reliability.• Scored for factual accuracy, coherence, safety, and instruction-following.• Contributed labeled data for LLM fine-tuning and RLHF workflows.• Maintained accuracy benchmarks and turnaround SLAs under high-volume workload.

2023 - 2024

Education

Work History