Sound Engineering Specialist - Freelance AI Trainer Project

United StatesOn-site

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Requirements

Are you a sound engineering expert eager to shape the future of AI? Large‑scale language models are evolving from clever chatbots into powerful tools for audio innovation.
With high‑quality training data, tomorrow’s AI can assist engineers in designing studio systems, optimizing acoustics, and solving real-world production challenges at scale.
That training data begins with you—we need your expertise to help power the next generation of AI.
A bachelor’s or master’s degree in Sound Engineering, Acoustical Engineering, Electrical Engineering (with audio specialization), or a related field is ideal; hands-on
experience with live sound rigs, studio installations, broadcast setups, or touring productions signals strong fit.
Familiarity with tools like Dante Controller, Smaart, QLab, and mixing consoles from Yamaha, Avid, or Allen & Heath is a plus.
Ready to turn your sound engineering expertise into the backbone of tomorrow’s AI? Apply today and help shape the tools that will power the next generation of audio technology.
Job title: Sound Engineering Specialist – AI Trainer

We offer a pay range of $8-to- $65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location.
Final offer amounts may vary from the pay range listed above.
benefits such as health insurance and PTO do not apply.

We’re looking for sound engineering specialists who live and breathe live sound reinforcement, microphone technique, signal flow, acoustics, mixing consoles, audio interfaces, and sound system calibration.
You’ll challenge advanced language models on topics like gain staging, PA tuning, time alignment, routing and patching, impedance matching, analog-to-digital conversion, and broadcast audio standards—documenting every failure mode so we can harden model reasoning.
On a typical day, you will converse with the model on complex audio system design and troubleshooting scenarios, validate engineering accuracy, identify gaps in signal-chain logic, and recommend improvements to prompt structures and model evaluation methods.