Reuben M.

Key Skills

Software

Appen

Labelbox

Top Subject Matter

Linguistics Domain Expertise

multilingual speech/ASR dataset QA (English & Swahili, East African speech variation)

Computer vision video annotation (object/activity boundaries)

Top Data Types

Audio

Video

Image

Text

Document

Top Task Types

Action Recognition

Freelancer Overview

Data Annotator (Freelance / Contract) — Appen. Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Appen and Labelbox. Education includes Master of Arts, Moi University (2024) and Bachelor's Degree, Moi University (2017). AI-training focus includes data types such as Audio and Video and labeling workflows including Evaluation, Rating, and Action Recognition.

Labeling Experience

Data Annotator (Freelance / Contract) — Appen

AppenAudio

Reviewed and evaluated speech datasets for ASR model training using accuracy targets above 98% across multiple concurrent task streams. Identified recurring failure patterns and edge cases, documenting findings that informed updates to labeling guidelines. Performed peer QA reviews to catch inconsistencies and escalate borderline cases with written justifications. • Applied evaluation rubrics to English and Swahili audio samples with independent linguistic judgment. • Analyzed inter-annotator differences and escalated ambiguous segments with rationale. • Measured quality through QA review cycles and maintained low error/late-submission rates. • Coordinated documentation of edge cases to improve downstream guideline clarity.

2025 - 2026

Data Annotator — Labelbox / CVAT Project

LabelboxVideoAction Recognition

Annotated complex video sequences frame-by-frame with attention to ambiguous object boundaries and activity classifications. Participated in inter-annotator agreement calibration sessions, analyzing disagreements to surface guideline gaps and improve consistency across the team. Reviewed sample outputs and documented edge cases in writing to refine rubrics for improved labeling quality. • Applied bounding box and segmentation standards across large-scale computer vision datasets. • Assessed ambiguous frames to keep classifications consistent under tight requirements. • Improved team guideline coverage by reporting disagreements and edge cases. • Contributed to downstream rubric updates based on observed annotation challenges.

2025 - 2025

Education

Work History