Audio Engineer

David AI•San Francisco, CA

6d•$120,000 - $160,000

About The Position

David AI builds and licenses voice and audio datasets for leading AI labs. The company is a roughly 50-person Series B team that collects data across approximately 20 language markets, including conversational, expressive and emotional text-to-speech, and transcription data. The Audio Engineer will serve as the in-house audio expert, setting audio standards and recording specifications, helping contributors and vendors achieve suitable recording setups, and overseeing hardware and studio aspects of in-person recording work. The role collaborates with Operations, Engineering, and Machine Learning teams and reports to the Head of Operations. The position also works with ML researchers to define audio events and quality issues, translate those definitions into labeling guidelines, and manage a QA team through iterative annotation and feedback cycles. It includes auditing annotation and post-processing models before production and defining acoustic targets for processed, synthetic, or AI-generated audio.

Requirements

At least 7 years of experience in audio engineering, recording, or production in studio, broadcast, podcast, post-production, or voice work.
A highly trained ear for subtle differences across voices, microphones, rooms, and signal chains.
Deep hands-on knowledge of microphones, interfaces, preamps, acoustics, and room treatment.
Strong DAW skills and fluency with loudness and metering, EQ, dynamics, noise reduction, and restoration tools such as iZotope RX.
Ability to troubleshoot remotely and explain fixes clearly to non-technical people.
Comfort working in a fast-moving startup with ambiguous problems, shifting priorities, and high ownership.

Nice To Haves

Experience with speech or voice data for AI, including TTS, ASR, or SFT projects, at a lab or data vendor.
Experience recording voice actors or directing dialogue sessions.
Experience supporting remote or distributed recording at scale.
Basic scripting skills in Python, ffmpeg, or SoX for batch checks or processing.
Multilingual.

Responsibilities

Serve as the audio authority for Operations, Engineering, and ML, diagnosing why recordings sound wrong.
Define audio quality standards and recording specifications for each program, including noise floor, clipping, reverb, levels, microphone placement, sample rate, and format.
Help contributors diagnose and improve home recording setups, including microphone choice and placement, gain staging, room treatment, and interface and software settings.
Write setup guides, onboarding checks, and troubleshooting documentation that non-experts can follow across the company's language markets.
Lead and calibrate a team to the company's audio standards; review problematic batches, identify root causes, and recommend fixes.
Define acoustic targets for processed, synthetic, or AI-generated audio and assess whether outputs meet those targets.
Work with ML researchers to identify and define audio events and acoustic quality issues.
Turn audio definitions into actionable guidelines for expert audio QA labelers.
Manage the QA team's iterative process of labeling audio samples, incorporating ML researcher feedback, and refining annotation processes for training and evaluation sets.
Conduct quality audits on annotation and post-processing models before they enter production.
Run the hardware and studio side of in-person recording work.