Janis Gloystein

• •Evaluate visual datasets and contribute domain-specific feedback to increase model accuracy for a top-tier AI lab.
• •Support model refinement through applied visual communication principles, critical thinking, and data analysis.

• •Act as both annotator and auditor to label and audit multimodal datasets, including audio processing and media data, meeting daily volume and quality targets.
• •Review and validate labeled datasets for accuracy and consistency, collaborating with data scientists to train 5 machine learning models achieving 97% precision and recall.
• •Flex between projects as demand requires, quickly adjusting to new project guidelines week-over-week while comprehending complex requirements to refine AI models.
• •Collaborate with team members to identify improvements to the labeling process to drive efficiency and promote high-quality outputs.
• •Perform QA using advanced annotation workflows (Labelbox) and contribute to rubric development and media A+B labeling.

• •Perform QA in annotation tooling, labeling and reviewing datasets to ensure high-quality structured outputs.
• •Operationalize on product feedback and apply continuous quality improvements aligned with annotation best practices.
• •Identify trends within large data sets, maintaining consistency and attention to detail across evolving project guidelines.

• •Designed and produced digital, print, web, and presentation materials requiring team-oriented collaboration and strict attention to detail.
• •Reviewed and optimized visual assets for accuracy, consistency, and multi-platform use across biotech, financial, and retail sectors.
• •Demonstrated excellent communication skills and self-directed workflow management while meeting strict production deadlines.

Awarded Outstanding Achievement in Design