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Review, evaluate, and annotate AI-generated text, image, audio, and video. Perform quality checks, identify edge cases, contribute high-quality labeled datasets, participate in calibration activities, and provide feedback to improve annotation guidelines and model performance.

Annotate and quality-review multimedia data (video, images, metadata) following structured labeling guidelines. Perform self-QA, participate in calibration, document ambiguous cases, track issues, support sampling and audits, and coordinate with stakeholders to meet throughput and quality targets. Work is remote and requires Turkish language proficiency.

Freelance Norwegian SEO Specialist responsible for keyword research and translation, metadata and content optimisation, on-page SEO, SEO copywriting, data management, and using analytics and third-party SEO tools to improve search performance.

Evaluate, annotate, and quality-check AI-generated text, images, audio, and video for Russian (Kazakhstan). Identify edge cases, ensure guideline compliance, participate in calibration, and provide feedback to improve datasets and workflows.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Evaluate AI outputs, audit model behavior, apply and refine labeling guidelines, identify failure modes and trends, document findings, and collaborate with cross-functional teams to improve model evaluation and data quality.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and validate data labeling outputs, provide structured feedback and hands-on training to annotators, monitor workflows and escalate issues, help apply and improve guidelines, and identify patterns in human and model outputs to maintain high-quality AI training data.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, enforce and improve guidelines, identify recurring issues and edge cases, escalate risks, and document feedback to support human-in-the-loop AI workflows.