Engineering San Francisco Full-time Senior level
This is a hands on audio authority role for someone who can hear what is wrong with a recording and explain the fix to people who are not audio experts. The work spans setting recording and quality standards, helping contributors and vendors fix home setups, running studio and hardware sessions, and leading a QA team that labels audio for machine learning. A strong candidate brings years of real recording and production experience, deep knowledge of mics, rooms and signal chains, and the patience to turn that knowledge into clear guides and audits.
How it fits the AI era
The company sells voice and audio data to AI labs, so the role sits close to model training: defining what counts as muffled or robotic audio and judging whether processed or generated output meets acoustic targets. AI tools speed up labeling and post processing, but the trained ear that sets the standards and catches subtle problems stays human. The job is largely about translating that human judgment into guidelines and checks that scale across many language markets.
How it builds AI experience
Working here means gaining direct exposure to speech data for TTS, ASR and related projects, and to the loop between ML researchers and audio QA. That experience comes from defining acoustic quality issues, auditing annotation and post processing models, and refining labeling guidelines. Scripting for batch checks and processing would add a practical edge for this kind of work.
Worth showing
- Critical listening and diagnosis
- Mic and room acoustics
- DAW and restoration tools
- Clear guides for non experts
- Leading audio QA teams
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About David AI
High-fidelity conversational audio dataset generation and speech research platform for foundation voice models, per the company.
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