AI Annotator

About Clear. Grid Clear. Grid is revolutionizing the debt resolution industry with cutting-edge AI and advanced analytics. We leverage real-time data, machine learning, and automation to optimize debt recovery for financial institutions. As we scale our technology platform, we are building high-performance, resilient systems to support our next phase of hyper-growth.

About the role

We are seeking an energetic Data Annotator (Fresh Graduate to 1–2 years experience) in Jordan to join our Data Annotation team. This role bridges the gap between linguistics and software/AI execution. You will handle daily ASR speech tasks focused on GCC accents (Emirati and Saudi), run priority LLM annotations, evaluate prompt performance, and work closely with our AI Research Engineers to monitor dataset quality and model output.

What you'll own (Responsibilities) Daily ASR & Dialect Annotation: Transcribe, audit, and label daily speech datasets, specializing in GCC spoken accents (Emirati and Saudi dialects). LLM Priority Annotation & Prompting: Execute high-priority LLM labeling, prompt testing, response evaluation, and post-call conversation auditing.

Data Quality Monitoring: Monitor ongoing dataset accuracy, run quality assurance checks, and log software/model edge cases. Engineering Collaboration: Work directly with AI Research Engineers to refine annotation guidelines, improve model performance, and test tool features.

What we're looking for

(Candidate profile) Experience Level: Fresh graduate or 1–2 years of experience in data annotation, AI operations, linguistic QA, or technical support. Language & GCC Accents: Strong linguistic intuition with high familiarity and comprehension of GCC spoken accents (Emirati and Saudi dialects). Tech & AI Literacy: Basic understanding of AI concepts (ASR, LLMs, prompt engineering) and comfort navigating modern software tools and workflows.

Communication & Writing: Strong written and verbal communication skills in both Arabic and English (assessed via a hands-on study case). Detail-Oriented: Methodical, adaptable, and focused on maintaining high standards of data accuracy.