Martin M.

Key Skills

Software

AWS SageMaker

Data Annotation Tech

Remotasks

Snorkel AI

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Top Task Types

Computer Programming/Coding

Data Collection

Evaluation/Rating

Prompt + Response Writing (SFT)

Freelancer Overview

Having finished my master's degree in Kenya and my undergraduate studies in the United States, I am a graduate of computer science with international academic experience. I have worked extensively in quality assurance, AI training workflows, and data labeling for both structured and unstructured data over the last four years. Tasks like entity recognition, classification, prompt-response evaluation, code annotation, and document-based NLP alignment are all part of my practical experience. I've worked on projects that evaluated educational content, classified programming languages, and used large language models (LLMs). I can accurately and efficiently handle large volumes of annotation thanks to my expertise with labeling platforms like Labelbox, Remotasks, and Scale AI tools. I contribute excellent technical writing abilities, a thorough understanding of programming principles, and the capacity to work with a variety of data formats, such as documents, code, and text. I can consistently produce high-quality outputs that support the development of advanced AI models thanks to my attention to detail, multilingual skills (English, Swahili, and French), and experience working with international QA teams.

Labeling Experience

Multimodal Data Labeling for LLM and Document-Based NLP Tasks

Scale AIDocumentQuestion AnsweringText Generation

Participated in a large-scale remote data annotation project focused on improving natural language models for educational and technical domains. Tasks included labeling entities in educational documents, categorizing technical queries, and evaluating AI-generated coding responses. Performed prompt-response quality checks for supervised fine-tuning (SFT) tasks and maintained high accuracy in programming-related annotations. The project involved over 25,000 labeled text/document entries with multi-tier quality assurance, peer reviews, and rubric-based validation cycles to ensure consistency.

2022 - 2024

Education

Work History