About the Role
We're looking for experienced Audio Engineers to help evaluate and improve AI systems trained on audio content. Your professional ear and Pro Tools expertise will directly shape how AI understands, produces, and refines sound — making a meaningful impact on the next generation of audio technology.
- Organization: Labelbox
- Type: Hourly Contract
- Location: Remote
- Commitment: 15+ hours/week, fully asynchronous
What You'll Do
- Produce, mix, and master high-quality audio content according to project specifications and industry standards
- Work efficiently in Pro Tools to edit, balance, and enhance multi-track recordings
- Review and refine audio for clarity, consistency, and technical accuracy
- Evaluate AI-assisted audio outputs for quality, alignment, and production integrity
- Provide structured, constructive feedback to improve AI-driven audio processing and production tools
Who You Are
- Experienced in audio engineering, sound design, or music production with a strong command of Pro Tools
- Access to professional-grade audio equipment and a quiet, acoustically treated workspace
- Proven ability to deliver high-quality mixes and masters efficiently and consistently
- Exceptional attention to detail in audio balance, clarity, and technical precision
- Curious about AI-driven audio processing, sound analysis, or machine learning applications in music and sound
Nice to Have
- Experience with audio dataset creation, labeling, or AI audio model training projects
- Familiarity with machine learning workflows or AI evaluation tasks
- Background in multiple audio genres or production styles
Why Join Us
- High impact — your craft directly improves AI audio models used by top research labs and Fortune 500 teams
- Full flexibility — work remotely, on your own schedule, from anywhere
- Clear ownership — know exactly what success looks like with autonomy to deliver your best work
- Growth potential — consistent top performers take the lead on new programs and mentor incoming experts
- Cutting-edge work — contribute to frontier AI projects at the intersection of audio and machine learning