About the role

We are hiring an Audio QA Lead to support the development of high-quality training datasets for next-generation voice AI models. In this role, you will work hands-on to improve the quality, consistency, and usability of speech datasets across applications such as text-to-speech, transcription, speech-to-speech, ASR, and conversational voice systems. Your work will directly influence how data is collected, reviewed, and delivered for real-world model training. You will work across three core areas: defining and applying audio quality standards, recording high-quality speech on demand, and performing annotation and QA across speech datasets. This is not a generic audio production role. The work focuses on making audio usable for model training and requires a strong understanding of how data quality impacts model performance.

What you'll do

• Develop, refine, and apply audio quality guidelines for speech and voice datasets.
• Review audio files against technical, linguistic, and task-specific standards, making clear approval, rejection, or revision decisions.
• Identify audio and annotation issues such as background noise, clipping, distortion, plosives, echo, low signal, segmentation errors, transcript mismatches, and speaker-label inconsistencies.
• Perform annotation and QA tasks, including transcription, timestamp validation, VAD/segmentation, diarization, pronunciation checks, and metadata review.
• Record speech based on provided scripts and performance guidelines, delivering natural, high-quality, specification-compliant audio.
• Document edge cases, update review rubrics, and improve internal SOPs and quality standards.
• Collaborate with research, ML, and operations teams to translate model requirements into data specifications and evaluation criteria.
• Ensure consistency and integrity across audio files, transcripts,...