Ambrose Tanui

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Polygon

Entity Ner Classification

Segmentation

Question Answering

Freelancer Overview

I am an experienced AI model training and technical evaluation specialist with a strong background in data annotation, prompt engineering, and reviewing AI-generated outputs. My expertise includes evaluating code snippets, web development workflows, and step-by-step technical reasoning for accuracy, clarity, and alignment with best practices. I am skilled in web development fundamentals (HTML, CSS, JavaScript), debugging, and code review, and I consistently deliver high-quality, structured feedback to improve AI model reliability and instructional quality. I thrive in remote, contract-based environments, collaborating with multidisciplinary teams and working independently to meet strict quality benchmarks and deadlines. My strong written communication skills enable me to explain technical concepts clearly to both technical and non-technical stakeholders, and I am committed to supporting long-term AI training and evaluation projects.

Labeling Experience

data annotation

Scale AIVideoBounding BoxPolygon

Contributed to large-scale AI training projects on Remotasks involving video and text datasets. Performed high-precision bounding box, polygon, and semantic segmentation annotations on dynamic video frames, ensuring strict adherence to ontology and quality standards. Conducted Named Entity Recognition (NER), classification, and question-answer validation tasks for LLM training datasets. Maintained 95%+ quality accuracy while meeting consistent throughput targets and following structured QA review workflows.

Contributed to large-scale AI training projects on Remotasks involving video and text datasets. Performed high-precision bounding box, polygon, and semantic segmentation annotations on dynamic video frames, ensuring strict adherence to ontology and quality standards. Conducted Named Entity Recognition (NER), classification, and question-answer validation tasks for LLM training datasets. Maintained 95%+ quality accuracy while meeting consistent throughput targets and following structured QA review workflows.

2023 - 2025

Education

Work History