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Arabic MSA Transcription Reviewer

Work from home Full-time role Hiring

About OpenTrain OpenTrain is the centralized platform where people start and grow careers in AI training and data labeling. We help freelancers discover projects, build a unified portfolio of annotation work, and find steady contract opportunities across the industry. We focus on real, paid AI-training roles—everything from transcription and annotation to review and quality assurance—so contributors can build durable freelance careers teaching AI. About AI training and why it matters AI systems learn from examples prepared and reviewed by people: the transcripts, tags, corrections, and annotations that shape model behavior. This human-side work is remote, flexible, and in high demand as AI products scale. As a transcription reviewer you will directly improve the quality of language data used to train speech and language models, helping systems better understand Modern Standard Arabic. The role OpenTrain is recruiting for a contractor to review, correct, and annotate Modern Standard Arabic (MSA) audio transcriptions. This entry-level, remote role fits independent freelancers with transcription experience and strong attention to linguistic detail. Work is part-time (contractor) with a commitment of 20+ hours per week. You will handle machine-generated and human-produced transcripts, apply consistent guidelines, and add metadata to support downstream processing. What you'll do

  • Transcribe Modern Standard Arabic audio with strong accuracy and fluency.
  • Review and correct machine-generated transcriptions for correctness, clarity, and consistency.
  • Add metadata tags and annotations to audio and text to support AI training pipelines.
  • Identify contextual cues and linguistic nuances in audio files and flag unclear segments.
  • Maintain data quality, confidentiality, and consistent application of transcription guidelines.
  • Provide written and verbal feedback on recurring data-quality issues when requested.

Requirements

You must be fluent in Modern Standard Arabic (written and spoken) and comfortable communicating in English. This role requires experience with audio transcription or similar language-data work, a strong eye for detail, and familiarity with metadata tagging and annotation practices.

  • Fluent Modern Standard Arabic, written and spoken.
  • Experience in audio transcription with strong attention to detail.
  • Ability to spot and correct transcription errors and enforce consistency.
  • Experience adding metadata tags and annotations for audio or text.
  • Strong written and verbal communication skills in Arabic and English.
  • Familiarity with transcription guidelines and language data quality best practices.
  • Comfort working independently in a remote contractor role.

Preferred / helpful background

  • Prior professional transcription experience.
  • Previous work on linguistic data projects or large-scale transcription efforts.
  • Audio engineering or annotation tooling experience.

Project details & pay Labeling tasks focus on audio transcription and data collection for Modern Standard Arabic. The project uses a third-party or custom annotation tool (listed as OTHER). Pay is hourly: up to $20.00/hr (hourly range listed $10–$20/hr). This is a contractor, part-time engagement; you must be available for 20+ hours per week and able to manage your own schedule.

  • Data type: Audio (MSA).
  • Label types: Transcription, Data Collection.
  • Employment: Contractor, Part-time.
  • Languages required: Arabic (ar) and English (en).
  • Location: Remote, worldwide applicants welcome.

How to apply If this matches your skills and availability, create or sign in to your OpenTrain account and submit your application to OpenTrain. Include details about your transcription experience, examples or samples if available, and your typical weekly availability. Successful applicants will be asked to follow project-specific guidelines and may complete a short qualification task to demonstrate consistency and accuracy before work begins.

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