Evaluate and annotate AI-generated Japanese songs for musicality, lyrics, vocals, and production in a part-time remote role. Immediate start; up to 6 months; 10+ hours/week; pay range $34.72–$62.49/hr.
Audio & Speech
100% Remote Hourly · $34.72–$62.49/hr
$34.72–$62.49/hr
Compensation
Worldwide
Eligibility
Intermediate
Experience
Jul 10, 2026
Posted
Open worldwide
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Why AI training matters (brief)
AI training — also called data labeling or annotation — is the human side of building AI. Contributors annotate and evaluate examples that teach models how to write, sing, mix, and understand creative media.
This work is remote, flexible, and accessible: many projects need domain knowledge more than formal credentials, and contributors directly shape how state-of-the-art systems behave.
The role
OpenTrain is hiring Japanese Music AI Evaluation Experts to review and score AI-generated music. You will evaluate lyrics, voice generation, and overall song quality, then annotate samples with structured taxonomy labels.
This is a part-time contractor role with immediate start, up to 6 months in duration, and a typical weekly commitment of 10+ hours (less than 20 hours/week). The work is fully remote and open worldwide.
Contract type: Contractor, Part-time
Duration: Up to 6 months, immediate start
Time: 10+ hours/week (under 20 hours/week)
Languages: Japanese (native/near-native) and English (ability to follow directions)
What you'll do
You will listen to AI-generated music samples and evaluate them on creative and technical dimensions, then record ratings and taxonomy labels using structured tasks.
Tasks include both side-by-side comparisons and single-sample assessments across musical dimensions and production attributes.
Rate musicality, creativity, and prompt adherence
Judge vocal quality and production/mix quality using audio-engineering terminology
Annotate genre, sub-genre, song structure, and vocal/instrumental characteristics
Assess the quality and usefulness of musical training data
Perform evaluation and classification labeling on audio samples
Requirements
You must have hands-on experience assessing or creating music and be fluent in Japanese and proficient in English to follow guidelines and write short notes in English when required.
Experience as a music producer, audio engineer, or sound mixer (required)
Strong understanding of musical performance, theory, and lyrics
Past experience writing lyrical music in Japanese
Native or near-native proficiency in Japanese and strong English comprehension
Ability to follow written directions in English and apply audio-engineering terminology
Helpful background and who should apply
This role suits experienced music creators and technical listeners who can distinguish vocal timbre, mix balance, and production artifacts while evaluating creativity and lyric content in Japanese.
A college degree in music or prior work in bilingual music contexts is a plus but not required if you have the practical skills above.
Compensation, labeling details, and logistics
Pay is hourly; the stated range in the project is $34.72–$62.49 per hour, with a top rate of $62.49/hr. Exact pay will be set at onboarding based on task and qualification.
Tasks involve audio data labeling with evaluation/rating and classification label types. The project accepts contributors worldwide and is fully remote.
Data type: Audio
Label types: Evaluation/Rating and Classification
Pay: $34.72–$62.49 per hour (top rate $62.49/hr)
Work location: Remote, worldwide
How it works — onboarding and workflow
If selected, you will receive project guidelines, examples, and a short qualification test to demonstrate consistent application of the taxonomy and rating scales.
Work is task-based: you will complete evaluation batches in a web interface, follow style and rating rubrics, and submit annotations according to the project's schema.
Onboarding includes guidelines, examples, and a qualification assessment
You will complete labeled evaluation batches through the OpenTrain system
Feedback and calibration may be provided to ensure rating consistency
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