Wesley Juma

Key Skills

Software

Remotasks

Scale AI

Label Studio

Top Subject Matter

No subject matter listed

Top Data Types

Image

Medical Dicom

Text

Top Label Types

Bounding Box

Polygon

Entity Ner Classification

Segmentation

Classification

Freelancer Overview

I am an experienced AI Data Annotator and Technical Reviewer with over four years specializing in data labeling, content moderation, and reinforcement learning from human feedback (RLHF). My expertise includes annotating and validating complex datasets for large language models, focusing on code generation, NLP, and intricate reasoning prompts. I consistently deliver high-quality, accurate annotations—maintaining a quality score above 98%—by applying strong attention to detail, fact-checking, and contextual analysis. Skilled in Python, SQL, and research automation tools, I have developed automated pipelines for large-scale data curation and led QA initiatives to ensure data integrity. My technical writing background ensures clear documentation and feedback, supporting continuous improvement of AI systems. I am fluent in English and Swahili, and I am passionate about contributing to high-performance AI projects.

Labeling Experience

Senior Data Annotator & QA

RemotasksImageBounding Box

I annotated over 50,000 images with 2D and 3D cuboids for autonomous driving datasets using Remotasks and Scale AI platforms. My responsibilities included ensuring tight-fitting boxes for objects like vehicles and pedestrians, and upholding pixel-perfect segmentation guidelines. I was also responsible for quality assurance and auditing the work of junior labelers. • Led LiDAR point cloud annotation for mapping and navigation • Maintained error rates below 1% throughout projects • Trained and mentored new annotators in proper labeling techniques • Performed segmentation for computer vision tasks

I annotated over 50,000 images with 2D and 3D cuboids for autonomous driving datasets using Remotasks and Scale AI platforms. My responsibilities included ensuring tight-fitting boxes for objects like vehicles and pedestrians, and upholding pixel-perfect segmentation guidelines. I was also responsible for quality assurance and auditing the work of junior labelers. • Led LiDAR point cloud annotation for mapping and navigation • Maintained error rates below 1% throughout projects • Trained and mentored new annotators in proper labeling techniques • Performed segmentation for computer vision tasks

2020

Custom Vehicle Detection Dataset Annotation

Scale AIImageBounding BoxPolygon

I manually annotated more than 2,000 street images using bounding boxes to help create a custom vehicle detection dataset for computer vision tasks. The annotation process required high accuracy and consistency to maximize model training effectiveness. I used Python tools to convert and validate annotation formats for compatibility. • Maintained high Intersection-over-Union (IoU) scores • Delivered annotations in XML, COCO, and YOLO formats • Documented annotation guidelines for reproducibility • Focused on urban and mixed-traffic detection scenarios

I manually annotated more than 2,000 street images using bounding boxes to help create a custom vehicle detection dataset for computer vision tasks. The annotation process required high accuracy and consistency to maximize model training effectiveness. I used Python tools to convert and validate annotation formats for compatibility. • Maintained high Intersection-over-Union (IoU) scores • Delivered annotations in XML, COCO, and YOLO formats • Documented annotation guidelines for reproducibility • Focused on urban and mixed-traffic detection scenarios

2023 - 2023

NLP Sentiment Analysis Labeling

Label StudioTextClassification

I labeled a dataset of 5,000 customer feedback sentences for sentiment analysis, assigning categories such as Positive, Negative, and Neutral. The role required careful consideration of contextual nuances within each sentence. My work improved the accuracy of the sentiment model for the client. • Developed and maintained a sentiment annotation guidebook • Conducted QA to minimize labeling drift • Handled customer support and feedback channels • Managed annotation in Label Studio

I labeled a dataset of 5,000 customer feedback sentences for sentiment analysis, assigning categories such as Positive, Negative, and Neutral. The role required careful consideration of contextual nuances within each sentence. My work improved the accuracy of the sentiment model for the client. • Developed and maintained a sentiment annotation guidebook • Conducted QA to minimize labeling drift • Handled customer support and feedback channels • Managed annotation in Label Studio

2022 - 2022

Freelance Data Labeler

Scale AIMedical DicomPolygonEntity Ner Classification

I performed semantic segmentation for medical imaging projects by precisely tracing polygons around anatomical regions of interest. My work contributed to training data for machine learning models in healthcare applications. I ensured accuracy and fast turnaround to meet client specifications. • Handled multiple medical imaging modalities • Complied with data privacy and client requirements • Delivered clear documentation with annotation files • Quality checked all submissions before delivery

I performed semantic segmentation for medical imaging projects by precisely tracing polygons around anatomical regions of interest. My work contributed to training data for machine learning models in healthcare applications. I ensured accuracy and fast turnaround to meet client specifications. • Handled multiple medical imaging modalities • Complied with data privacy and client requirements • Delivered clear documentation with annotation files • Quality checked all submissions before delivery

2019 - 2020

Education

Work History