Data Labeler - Music

As a Data Labeler - Music at ElevenLabs, your primary responsibilities will include reviewing audio assets, applying detailed descriptive labels, and enhancing our machine learning models for high-quality results. Your exceptional musical taste and understanding of music theory will be instrumental in classifying and annotating various musical elements effectively.

To excel as a Data Labeler - Music at ElevenLabs, you should have comprehensive genre knowledge, expertise in musical structure and emotion, and a strong understanding of music theory. Additionally, proficiency in reading music notation and having a sharp ear for pitch and melody is crucial for successfully performing your duties.

Success as a Data Labeler - Music at ElevenLabs requires a mix of analytical and creative skills. Being detail-oriented, having strong problem-solving capabilities, and the ability to work autonomously while delivering high-quality work quickly will help you thrive in this role.

Yes, the Data Labeler - Music position at ElevenLabs is remote, which means you can work from anywhere in the world. We prioritize talent over location and have structured asynchronous workflows to maximize your impact with minimal meetings.

At ElevenLabs, the culture is characterized by high-velocity innovation, autonomy, and a collaborative spirit. Joining our global team means working alongside passionate individuals from over 30 countries, with a focus on continuous growth, learning, and pushing the boundaries of audio technology.

In this role, it's essential to demonstrate your familiarity with various music genres and how they influence your labeling process. Discuss specific genres you've worked with and how your knowledge helps you accurately annotate music elements that impact AI learning.

Highlight your proficiency in music theory, reading music notation, and your ability to analyze and identify musical elements accurately. Providing examples of experiences where these skills were beneficial can strengthen your response.

Explain your approach to analyzing audio assets meticulously. You might mention using ear training techniques or how you remain focused on the details, ensuring that your annotations align precisely with the elements in the music.

Discuss any tools, software, or specific audio analysis programs you’re familiar with that assist in music data labeling. If you have experience with specific machine learning platforms or audio editing software, be sure to mention those.

Emphasize your openness to feedback and how you incorporate it to improve your labeling processes. Providing a specific instance where you successfully adapted based on feedback would illustrate your receptivity and growth-minded attitude.

Share a specific experience that required you to go above and beyond in analyzing or labeling audio data. This could involve working on a project with tight deadlines, complex genres, or collaborating with others to achieve a shared goal.

Convey your methods for keeping up with industry trends, whether through online courses, webinars, music discovery platforms, or engaging with communities of music and audio professionals.

Discuss your motivation for wanting to work in audio AI, possibly mentioning how it aligns with your background in music and technology, and your belief in the power of making music more accessible globally.

Describe your strategies for maintaining productivity in a remote setting, such as using task management tools, setting deadlines for yourself, or establishing routines that help you stay focused on deliverables.

Highlight the importance of accurate data labeling and how it impacts the performance of machine learning models. You could discuss how your attention to detail and understanding of music enhances the quality of the datasets used in training AI.