Hiring nowSpeech, audio and music intelligence

Audio Intelligence Scientist

Also called Audio ML Scientist

An Audio Intelligence Scientist builds models that recognise sound events and reason about acoustic scenes. They make audio features dependable in real settings, where noise, rooms and microphones keep changing.

What they do

They define which sounds matter and how to label the scene around them. They train and adapt audio models, often Transformer based, and build the data pipelines behind them. Much of the week goes on evaluation: assembling test sets that cover background noise, different rooms and different devices, then working out why the model fails on a given clip. They run listening tests where human judgement is needed. They compare approaches, trim models for the target device, and explain the results to product and engineering colleagues in plain terms.

What a strong hire delivers

In the first months, a strong hire delivers a working audio event model and a benchmark the team trusts. They agree the sound categories with product, build test sets that reflect real devices and settings, and report detection quality with clear error breakdowns. They leave a repeatable evaluation process, a short list of known failure cases, and a sensible plan for the next round of improvement.

How you can tell they are good

Ask them to walk through an audio model they shipped or tested. Good people explain how they built the benchmark, what they held out, and how they checked it against real device and background variation. They talk about failures before successes. Ask how they would know a score was misleading. Be wary of anyone who quotes only a headline accuracy figure on a clean public dataset.

Why it is hard to fill

The title varies widely, and many strong people sit under speech, acoustics or music titles. Few have worked on non-speech sound at depth, so the pool is small and mixed. Candidates tend to be strong on modelling or on acoustics, rarely both. Evaluation discipline is the real gap. We screen for it early, because a CV rarely shows it.

Where to find them

Look at speech recognition and speech processing teams, acoustics and signal processing groups, and music technology labs. Many come from research settings and applied ML teams in consumer devices, smart home, automotive, hearing technology and media companies. Some move over from audio engineering or bioacoustics. Titles include audio ML scientist, speech scientist, research engineer and acoustic modelling engineer.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an Audio Intelligence Scientist

Tell me about an audio event or scene model you took from idea to real use. What was hardest?
A strong answer names the sounds, the data, the deployment setting and one real obstacle, such as noise or device differences. They own the decisions made. Worry if the story is only about architecture or only about a public dataset.
How would you build a benchmark for detecting a sound across different rooms, microphones and background noise?
Look for deliberate coverage of devices, environments and overlapping sounds, clean separation of train and test sources, and attention to label quality. Worry if they suggest a random split of clips from the same recordings.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for an Audio Intelligence Scientist to build models that recognise sound events and understand acoustic scenes. You will work with product and engineering to make detection dependable across the devices and environments our customers actually use.

What you will do

Define sound event categories and labelling approaches with product colleagues
Train and adapt deep learning models for audio event detection and scene analysis
Build and maintain benchmarks covering background noise, rooms and device variation
Analyse errors and report detection quality clearly
Run listening tests where human judgement is needed
Prepare models for efficient use on target devices
Document methods so others can repeat and extend the work

What you will bring

Demonstrated work building audio models, such as event detection, classification or scene analysis
Strong grounding in deep learning, including Transformer based approaches
Experience designing evaluation sets and explaining their limits
Working knowledge of acoustics and audio signal processing
Fluent use of audio toolchains and common ML frameworks
Clear written and spoken communication with non-specialists

Nice to have

Experience with music or non-speech audio
Experience running listening-test platforms
Experience deploying models on constrained hardware

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

Speech and audio modellingAdvancedRecognise, synthesise or analyse audio
Deep learningAdvancedBuild and diagnose trainable neural models
Linguistics and localisationWorkingAdapt language behaviour across cultural and linguistic contexts
Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

Generative AI: Navigating intellectual property, World Intellectual Property Organization
Transformers documentation, Hugging Face

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