Kiwanuka M.

Key Skills

Software

Label Studio

Appen

Top Subject Matter

Logical & Structured Reasoning

Software / Code Annotation

Technical Writing & Documentation Review

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Bounding Box

Freelancer Overview

Senior Data Annotation Specialist — Maximus. Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Label Studio and Appen. Education includes Certificate in Software Engineering, Refactory Academy (2025). AI-training focus includes data types such as Text and Image and labeling workflows including RLHF and Bounding Box.

Labeling Experience

Senior Data Annotation Specialist — Maximus

Label StudioTextRLHF

Performed RLHF preference-ranking annotation for a Tier-1 LLM client by generating preference outputs from prompt-response pairs. Maintained high-quality standards, including zero rework requests from the client QA team and 100% inter-annotator agreement for NER tasks over a 90-day sprint. Improved team efficiency and model safety by authoring detailed multilingual guidelines and flagging systematic labeling errors early before delivery. • Led a team of 12 annotators on a large-scale RLHF project (14,000+ prompt-response pairs). • Maintained 100% inter-annotator agreement benchmark for Named Entity Recognition tasks. • Authored a 40-page multilingual sentiment annotation guideline document covering English, Swahili, and Luganda. • Identified a label-bleed error in a 6,000-sample medical NLP dataset prior to delivery.

2022 - Present

Data Labeling Analyst — Appen

AppenImageBounding Box

Annotated images for an autonomous vehicle perception model, including bounding boxes and related traffic/pedestrian labeling. Achieved 100% accuracy on final QA review and progressed to an internal reviewer role for continuous quality auditing. Improved annotation operations by contributing to a LiDAR point cloud labeling workflow that reduced average task completion time and was later adopted more broadly. • Annotated 22,000+ images including bounding boxes, lane markings, traffic sign classification, and pedestrian keypoints. • Reviewed 800+ submissions per week as an internal reviewer, maintaining reject rates below the 2% threshold. • Supported workflow creation for LiDAR point cloud labeling to improve throughput. • Helped complete assigned tasks ahead of deadlines, enabling contract extension and performance bonus.

2020 - 2022

Education

Work History