Newton Maniak

I’m a detail-oriented AI Analyst with experience evaluating AI-generated content for quality, factual accuracy, relevance, and consistency. I follow detailed evaluation guidelines to ensure reliable, structured outputs that directly support AI model improvement.

In my recent roles at RWS Group and across multiple annotation-focused projects, I documented evaluation results using spreadsheets and structured reporting tools. I also performed quality checks to help maintain dependable datasets and improve annotation consistency through quality calibration exercises.

My work as a Data Annotation Specialist includes labeling and reviewing text datasets for machine learning applications, verifying annotations, and resolving inconsistencies based on project requirements. I’ve also evaluated AI-generated content for accuracy, completeness, and policy adherence, including flagging low-quality, duplicate, and out-of-scope submissions.

I’m skilled in analyzing model responses, maintaining high-quality datasets, and communicating clearly with the ability to deliver consistent evaluations. I’m comfortable working independently in remote environments and I’m available for full-time contract work where structured evaluation workflows matter.