Miriam Njogu

I am an experienced and detail-oriented AI Trainer with over 4 years of hands-on experience in data labeling, image and text segmentation, annotation, and dataset curation for machine learning models. I work across image, text, and audio data types and support training pipelines for projects in autonomous driving, healthcare imaging, e-commerce recommendation, and natural language processing.

I have strong proficiency with annotation and dataset management tools including Labelbox, CVAT, SuperAnnotate, Supervisely, V7, and Prodigy, and apply rigorous quality assurance through annotation QA, consensus validation, and feedback loops. I collaborate closely with machine learning engineers to define labeling guidelines and ensure consistency and accuracy in annotations.

I bring practical familiarity with project tools such as Jira, Trello, Slack and Google Sheets, and a basic technical foundation in Python, JSON, and Google Colab. I hold a Bachelor's in Business and IT and certifications from Google and AWS, and I am focused on delivering high-quality labeled datasets that accelerate model development.