David M.

Key Skills

Software

CVAT

Supervisely

Appen

Clickworker

CloudFactory

CrowdSource

Dataloop

Encord

Google Cloud Vertex AI

HiveMind

iMerit

Img Lab

LabelImg

Label Studio

Micro1

Mindrift

OneForma

Remotasks

Sama

Surge AI

Toloka

Telus

V7 Labs

Top Subject Matter

AI/ML training data quality validation and fact-checking

Video-to-text annotation for AI training

Multi-modal AI dataset annotation

Top Data Types

Text

Video

Image

Document

Top Task Types

Text Generation

Bounding Box

Polygon

Segmentation

Classification

Entity (NER) Classification

Point/Key Point

Polyline

Cuboid

Object Detection

Question Answering

Text Summarization

RLHF

Fine-tuning

Red Teaming

Transcription

Evaluation/Rating

Data Collection

Prompt + Response Writing (SFT)

Freelancer Overview

AI data annotation and research professional with 6+ years of experience supporting AI and machine learning projects through structured labeling, fact-checking, evaluation, and quality assurance workflows across text, image, audio, and video datasets. Experienced in video-to-text timestamp annotation, prompt evaluation, intent verification, and rubric-based quality reviews for production-level AI training projects. Core strengths include CVAT, Labelbox, Supervisely, Hasty.ai, SuperAnnotate, Data Compute, and Editor Label. Professional background includes remote AI contractor and annotation roles with organizations such as Label Your Data, CloudFactory, Micro1, and SmartOne.ai, supporting AI model training, dataset validation, peer review, and guideline refinement. Education includes a Bachelor of Science in Applied Statistics with IT from Maseno University (2016). AI training experience covers data types such as Text, Image, Audio, and Video, with workflows involving Evaluation, Rating, Prompt Review, Fact-Checking, and Text Generation.

Labeling Experience

AI Contractor / Business Analyst

TextFine-tuning

• Analyze AI model outputs, business workflows, and user requirements to improve data quality, prompt accuracy, and operational efficiency across LLM training projects. • Review and validate AI-generated responses, annotations, and evaluation datasets to ensure compliance with project guidelines, factual accuracy, and quality assurance standards. • Collaborate with cross-functional teams to identify process gaps, document business requirements, generate performance insights, and support AI model optimization through structured feedback and data analysis.

2026 - Present

Data Labelling Specialist, Micro1 (Remote, Contract)

VideoText Generation

Conducted video-to-text annotations with timestamping to support downstream AI training. Built natural language scripts aligned with cultural and contextual expectations for improved model performance. Performed quality checks and iterative refinements to raise annotation accuracy and consistency. • Timestamped video-to-text annotations • Wrote training scripts with cultural/contextual alignment • Performed quality checks and refined outputs • Improved performance through iterative review cycles

2025 - 2025

Independent Contractor, Label Your Data (Remote)

Text

Provided AI data annotation and validation for text datasets in line with project instructions and confidentiality requirements. Performed fact-checking and online research to confirm data correctness before submission. Supported consistent quality by following rubrics and resolving guideline ambiguities with the team. • Annotated using multiple annotation workflows and tools • Validated text outputs via online research • Assisted with task allocation and delivery management • Ensured adherence to strict guidelines and confidentiality

2025 - 2025

Data Annotator / Researcher, Cloudfactory (Remote, Kenya)

CVATImage

Annotated and fact-checked multi-modal datasets (image, text, audio, and video) to support AI model training. Verified AI-generated responses through structured research and intent validation, then reviewed peer work to identify errors and improvements. Collaborated with QA teams and scientists to refine annotation guidelines and maintain production-level quality. • Fact-checking and intent verification for AI outputs • Peer review with error flagging and actionable feedback • Rubric/guideline refinement with QA and scientists • Prompt generation and LLM response testing for optimized interactions

2019 - 2025

Quality Review Annotator, SmartOne.ai (Remote, Pilot Project)

Video

Reviewed and audited video annotation outputs against rubric-based guidelines to ensure quality and consistency. Delivered structured feedback through collaborative review workflows while following confidentiality protocols. Ensured annotated materials met production requirements before release. • Rubric-based auditing of video annotations • Structured feedback for quality improvement • Collaborative reviews and guideline compliance • Maintained confidentiality during audits

2024 - 2024

Education

Work History