Audio AI Specialist (Speech Data, Quality & Annotation)

• HireArt is helping our client find an Audio AI Specialist (Speech Data, Quality & Annotation) to support the development of high-quality training datasets for next-generation voice AI models.
• In this role, you’ll 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, this work focuses on making audio usable for model training and requires a strong understanding of how data quality impacts model performance.
• 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.

As an Audio AI Specialist, you’ll:

• 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., * They want someone with the ability to identify some quirks in audio (both aurally and visually), and experience with annotation/QA Experience with tools such as Audacity, Praat, or similar audio inspection tools Basic Python, Bash, or SQL for QA checks and dataset review Background in linguistics, phonetics, speech research, or voiceover
• Experience reviewing both real and synthetic audio Multilingual experience or familiarity with accent and dialect variation Familiarity with consented, licensed, and compliant handling of voice data

Daily tasks

• Build, refine, and consistently apply audio quality guidelines for speech and voice datasets Review audio files against technical, linguistic, and task-specific standards, and make clear approval, rejection, or revision decisions Identify issues such as background noise, clipping, distortion, plosives, echo, low signal, channel imbalance, unnatural silence, overlap, segmentation errors, transcript mismatches, and speaker-label errors
• Perform annotation and QA tasks including transcription, timestamping, VAD/segmentation, diarization, pronunciation checks, and metadata validation Record speech on demand based on provided scripts and performance guidelines, delivering natural, clean, spec-compliant audio Document edge cases, update review rubrics, and improve internal SOPs and examples Partner with research, ML, and operations teams to turn model needs into data specifications, quality thresholds, and evaluation criteria Help maintain consistency and integrity across raw audio, transcripts, annotations, and metadata

• Direct experience in audio AI training is required Hands-on experience working on datasets or evaluation workflows for TTS, transcription, ASR, speech-to-speech, or related voice AI systems
• Proven experience building or applying audio quality guidelines in a production workflow Proven experience with speech annotation tasks such as transcription, timestamp QA, VAD/segmentation, and diarization
• Strong ear for audio quality and naturalness, with the ability to catch subtle issues consistently Ability to produce high-quality recordings from a quiet environment using professional or near-professional recording equipment
• Strong written communication skills and ability to write precise, actionable feedback High attention to detail and strong judgment when reviewing edge cases Comfortable working with structured data and metadata in spreadsheets, CSV, or JSON workflows