Besimple AI
Audio QA Lead - Part Time Contractor
New
USALeadRemoteaudio annotationquality assurancetranscriptiontimestamp validationdiarizationpronunciation checking
Lead and perform quality assurance tasks for audio datasets used in training voice AI models.
Responsibilities
- 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.
- 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.
Nice to have
- 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.
Other
- 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.