Dennis Muriuki

Key Skills

Software

Scale AI

Internal/Proprietary Tooling

Data Annotation Tech

Other

Top Subject Matter

General/healthcare/scientific Domain Expertise

Healthcare/pharmaceutical/general Domain Expertise

Legal Services & Contract Review

Top Data Types

Text

Video

Document

Top Task Types

Action Recognition

Classification

Freelancer Overview

"AI data annotator and LLM evaluator with active experience across DataAnnotation, Shaip, and Atlas Capture (Tier 2). Specialising in text evaluation, RLHF feedback, hallucination detection, and healthcare domain annotation."

Labeling Experience

AI Knowledge Specialist - Tier 2 Contributor

OtherText

Evaluated LLM outputs for factual accuracy, reasoning, and compliance using advanced annotation protocols. Applied clinical and pharmaceutical subject matter expertise to specialized health task annotation. Flagged hallucinations and errors before final data inclusion to support downstream AI applications. • Contributed to quality evaluation of complex model outputs. • Engineered prompts across varied LLM and ML workflows. • Maintained zero-error discipline in async environments. • Performed annotation of complex human action sequences for ML model development.

Evaluated LLM outputs for factual accuracy, reasoning, and compliance using advanced annotation protocols. Applied clinical and pharmaceutical subject matter expertise to specialized health task annotation. Flagged hallucinations and errors before final data inclusion to support downstream AI applications. • Contributed to quality evaluation of complex model outputs. • Engineered prompts across varied LLM and ML workflows. • Maintained zero-error discipline in async environments. • Performed annotation of complex human action sequences for ML model development.

2023 - Present

AI Data Annotator & LLM Evaluator

Data Annotation TechText

Evaluated LLM-generated responses for multiple criteria, including accuracy, coherence, adherence to instructions, and tone across diverse task categories. Consistently provided comparative rankings and detailed rationales, forming part of RLHF training pipelines. Flagged hallucinations, logical issues, and violations using detailed annotations for continuous LLM improvement. • Applied healthcare and pharmaceutical knowledge to ensure precision on medical and scientific tasks. • Focused on actionable feedback over binary pass/fail outputs for nuanced model improvement. • Contributed structured human feedback for reinforcement learning. • Tasks included evaluation of outputs for coding, reasoning, creative writing, and Q&A.

Evaluated LLM-generated responses for multiple criteria, including accuracy, coherence, adherence to instructions, and tone across diverse task categories. Consistently provided comparative rankings and detailed rationales, forming part of RLHF training pipelines. Flagged hallucinations, logical issues, and violations using detailed annotations for continuous LLM improvement. • Applied healthcare and pharmaceutical knowledge to ensure precision on medical and scientific tasks. • Focused on actionable feedback over binary pass/fail outputs for nuanced model improvement. • Contributed structured human feedback for reinforcement learning. • Tasks included evaluation of outputs for coding, reasoning, creative writing, and Q&A.

2023 - Present

LLM Output Evaluation & Healthcare Domain Annotation — Atlas Capture Platform

Scale AIVideoAction Recognition

Worked as a Tier 2 qualified contributor on the Atlas Capture platform, advancing beyond Tier 1 after passing all required accuracy assessments — a competitive upgrade that a significant proportion of contributors do not achieve. Core work involved evaluating large language model outputs for factual accuracy, identifying hallucinations and confident but incorrect reasoning, and flagging errors before they could propagate into training data or downstream AI applications. Applied pharmaceutical and clinical expertise (ACLS, PALS, BLS certified) to healthcare-specific annotation tasks, providing subject matter accuracy that general contributors cannot replicate. This included verifying medical claims against clinical ground truth, assessing drug-related AI outputs for accuracy, and ensuring health-related training data met the precision standards required for medical AI deployment.

Worked as a Tier 2 qualified contributor on the Atlas Capture platform, advancing beyond Tier 1 after passing all required accuracy assessments — a competitive upgrade that a significant proportion of contributors do not achieve. Core work involved evaluating large language model outputs for factual accuracy, identifying hallucinations and confident but incorrect reasoning, and flagging errors before they could propagate into training data or downstream AI applications. Applied pharmaceutical and clinical expertise (ACLS, PALS, BLS certified) to healthcare-specific annotation tasks, providing subject matter accuracy that general contributors cannot replicate. This included verifying medical claims against clinical ground truth, assessing drug-related AI outputs for accuracy, and ensuring health-related training data met the precision standards required for medical AI deployment.

2023

Data Capture, Verification & Structured Data Delivery — KNEC National Contract

Internal Proprietary ToolingTextClassification

Selected by the Kenya National Examinations Council (KNEC) for a national-level data capture contract, operating under strict government-grade institutional accuracy standards. The role required normalising inconsistent, high-volume source records into clean, structured digital outputs delivered to precise specification — with zero tolerance for error given the institutional consequences of inaccurate examination data. Delivered consistent, verified output across a compressed two-week high-volume contract, demonstrating the ability to sustain accuracy under significant time pressure across diverse and varied input types. Every record was cross-checked before submission, applying the same multi-source verification discipline developed through years of pharmaceutical prescription checking and clinical documentation.

Selected by the Kenya National Examinations Council (KNEC) for a national-level data capture contract, operating under strict government-grade institutional accuracy standards. The role required normalising inconsistent, high-volume source records into clean, structured digital outputs delivered to precise specification — with zero tolerance for error given the institutional consequences of inaccurate examination data. Delivered consistent, verified output across a compressed two-week high-volume contract, demonstrating the ability to sustain accuracy under significant time pressure across diverse and varied input types. Every record was cross-checked before submission, applying the same multi-source verification discipline developed through years of pharmaceutical prescription checking and clinical documentation.

2025 - 2025

Education

Work History