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.
This is a part-time contractor role that can turn into full-time role.
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, annotations, and associated metadata.
Who we're looking for
The ideal candidate has direct experience working with audio AI datasets and understands what makes speech data effective for model training. You have a strong ear for audio quality, are comfortable applying annotation standards, and can consistently produce and evaluate high-quality recordings.
Direct experience working with audio AI training datasets or evaluation workflows.
Hands-on experience with TTS, ASR, transcription, speech-to-speech, or related voice AI systems.
Experience developing or applying audio quality standards in production environments.
Experience with speech annotation tasks such as transcription, timestamp QA, VAD/segmentation, and diarization.
Strong auditory judgment with the ability to consistently identify subtle audio quality issues.
Ability to produce high-quality recordings in a controlled, quiet environment using professional or near-professional equipment.
Strong written communication skills with the ability to provide clear, actionable feedback.
High attention to detail and sound judgment when evaluating edge cases.
Comfort working with structured data formats such as spreadsheets, CSV, or JSON.
Bonus qualifications
Experience with audio tools such as Audacity, Praat, or similar.
Basic scripting skills in Python, Bash, or SQL for QA or dataset analysis.
Background in linguistics, phonetics, speech research, or voiceover work.
Experience evaluating both real and synthetic audio.
Multilingual experience or familiarity with accents and dialect variation.
Familiarity with compliant handling of consented and licensed voice data.