Audio QA Lead - Part Time Contractor

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.