Abigail Ebuk

I am a detail-oriented Annotation Engineer with 3 years of professional, hands-on experience in annotating, reviewing, labeling and delivering high-accuracy data labeling, quality checks, and process improvements for machine learning and AI projects. I have proven expertise working with international clients, ensuring data integrity, workflow optimization, and making impactful contributions to model training and analysis. My work includes accurately annotating over 100,000 data points (text, images, audio) with 99%+ accuracy, significantly improving machine learning model performance. I implement quality assurance protocols that reduce annotation errors by 20% and optimize review workflows. I'm proficient in various annotation tools like Labelbox, Amazon SageMaker Ground Truth, CVAT, and Supervisely, and can utilize Python for scripting/automation and SQL for basic querying. I excel at collaborating with data scientists to refine processes, resulting in faster project turnaround and robust training data. I've also spearheaded process optimizations, leading to a 15% improvement in annotation efficiency, and authored team guideline manuals to reduce training times for new annotators. My commitment to detail ensures a 97% validation pass rate from downstream users.