Jennifer Ann

I've evaluated more than 20,000 AI-generated responses at Sata Group, focusing on technical accuracy, logical reasoning, instruction-following, and code quality across computer science and programming domains.

I assess Python, JavaScript, C, C++, and SQL output for efficiency, security vulnerabilities, completeness, and best practices. Through RLHF workflows, response ranking, fact-checking, and actionable technical feedback, I've helped improve AI-generated code while maintaining 90%+ quality scores.

My annotation work includes 7,000+ retail images at Appen AI with 96% accuracy and 10,000+ text samples for machine-learning classification research at Colorado State University. I also bring content review, technical transcription, guideline documentation, and quality-control experience.

Alongside AI training work, I've built 15+ full-stack web applications for clients using Django, React, Node.js, SQL, SQLite, and PostgreSQL. I'm available for remote AI training projects and bring a Computer Science foundation, careful technical judgment, and clear written feedback.