Multimodal Content Evaluator

Job Description Evaluate LLM outputs across multiple modalities, including text, image captions, video descriptions, and multimodal prompts. Assess quality against project-specific criteria such as correctness, coherence, completeness, style, cultural appropriateness, and safety. Identify subtle errors, hallucinations, or biases in AI responses and provide detailed written feedback, tagging, and scoring to ensure consistency across the evaluation team. Collaborate with Project Managers and Quality Leads to meet accuracy, reliability, and turnaround benchmarks. Required Skills and Qualifications Bachelor's degree/diploma or equivalent educational qualification. 1+ years of experience in data annotation, LLM evaluation, content moderation, or related AI/ML domains. Demonstrated experience working with data annotation tools and software platforms. Strong understanding of language and multimodal communication (instruction following in image generation, fact-checking, narrative coherence in video, etc.). Ability to adapt quickly to changing project directions and fast-paced work environments. Previous experience creating or annotating complex data specifically for Large Language Model (LLM) training is a plus.