Abdulsomod Badmus

Key Skills

Software

Appen

Remotasks

Surge AI

Top Subject Matter

General Domain Expertise

Medical Domain Expertise

Scientific Domain Expertise

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Classification

Freelancer Overview

AI Data Annotator & Content Evaluator (Freelance). Brings 7+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Appen, Remotasks, and Labelbox. Education includes Bachelor of Pharmacy, Obafemi Awolowo University (2026). AI-training focus includes data types such as Text and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

AI Data Annotator & Content Evaluator (Freelance)

AppenTextRLHF

As an AI Data Annotator & Content Evaluator, I reviewed and labelled over 5,000 datasets across text, image, and instructional samples to meet stringent quality benchmarks. My responsibilities included evaluating AI-generated responses for factual accuracy, prompt adherence, and logical coherence using established frameworks. I collaborated with QA leads, performed RLHF tasks, and maintained detailed annotation logs ensuring high inter-rater reliability. • Labelled diverse datasets using Appen, Remotasks, Surge AI, and Labelbox software. • Assessed AI responses against the 3H (helpfulness, harmlessness, honesty) rubric and flagged ambiguity patterns. • Regularly contributed medical and scientific expertise to annotation projects, especially in healthcare and pharmacology. • Reduced annotation inconsistency rates through calibration sessions with QA leads.

As an AI Data Annotator & Content Evaluator, I reviewed and labelled over 5,000 datasets across text, image, and instructional samples to meet stringent quality benchmarks. My responsibilities included evaluating AI-generated responses for factual accuracy, prompt adherence, and logical coherence using established frameworks. I collaborated with QA leads, performed RLHF tasks, and maintained detailed annotation logs ensuring high inter-rater reliability. • Labelled diverse datasets using Appen, Remotasks, Surge AI, and Labelbox software. • Assessed AI responses against the 3H (helpfulness, harmlessness, honesty) rubric and flagged ambiguity patterns. • Regularly contributed medical and scientific expertise to annotation projects, especially in healthcare and pharmacology. • Reduced annotation inconsistency rates through calibration sessions with QA leads.

2023 - 2025

Scientific Content Fact-Checking & Data Labeling

LabelboxTextClassification

In the Scientific Content Fact-Checking & Data Labeling project, I independently validated medical and pharmacological claims generated by AI systems. I labelled statements as accurate, partially accurate, or hallucinated, meticulously documenting supporting evidence for each annotation. This task culminated in a structured dataset of 200+ annotated claim-evidence pairs showcasing rigorous, expert annotation. • Produced and curated a shared claim-evidence annotation dataset. • Applied domain expertise to flag AI hallucinations and misinformation. • Utilized annotation and data-management tools from previous roles including Labelbox. • Developed portfolio materials that demonstrate scientific data labeling.

In the Scientific Content Fact-Checking & Data Labeling project, I independently validated medical and pharmacological claims generated by AI systems. I labelled statements as accurate, partially accurate, or hallucinated, meticulously documenting supporting evidence for each annotation. This task culminated in a structured dataset of 200+ annotated claim-evidence pairs showcasing rigorous, expert annotation. • Produced and curated a shared claim-evidence annotation dataset. • Applied domain expertise to flag AI hallucinations and misinformation. • Utilized annotation and data-management tools from previous roles including Labelbox. • Developed portfolio materials that demonstrate scientific data labeling.

2024 - 2024

AI Prompt Quality Evaluation Project

AppenText

For the AI Prompt Quality Evaluation Project, I evaluated more than 1,000 AI-generated text completions covering fields such as healthcare, law, and general knowledge. My role involved scoring outputs for accuracy, clarity, and fidelity to instructions using a personally developed scoring rubric. This systematic evaluation process contributed directly to improving AI model quality and consistency. • Developed scoring rubrics modeled after industry standards to guide evaluations. • Provided structured, domain-specific feedback for varied subject matters. • Supported throughput and velocity by implementing a reproducible assessment system. • Used annotation platforms such as Labelbox, Surge AI, and Appen.

For the AI Prompt Quality Evaluation Project, I evaluated more than 1,000 AI-generated text completions covering fields such as healthcare, law, and general knowledge. My role involved scoring outputs for accuracy, clarity, and fidelity to instructions using a personally developed scoring rubric. This systematic evaluation process contributed directly to improving AI model quality and consistency. • Developed scoring rubrics modeled after industry standards to guide evaluations. • Provided structured, domain-specific feedback for varied subject matters. • Supported throughput and velocity by implementing a reproducible assessment system. • Used annotation platforms such as Labelbox, Surge AI, and Appen.

2023 - 2023

Education

Work History