Applied ML Engineer, Audio

Stealth Startup; Stockholms län; On-site; SEK 700,000 - 1,000,000; Full time

Job summary

Stealth Startup is seeking an exceptional Applied ML Engineer to translate audio research into user-visible improvements. You will own the journey from data to production, building datasets and listening tests, training models, investigating failures, and shipping enhancements to real users.

We value hands-on experience with speech/audio models, real-world noisy data, robust evaluation, and the ability to move models from experimentation into production.

Qualifications

• Hands-on experience training speech or audio models.
• Experience with real-world, imperfect audio not only benchmark datasets.
• Solid understanding of evaluation, including perceptual listening tests.
• Ability to take models from experimentation into production.
• First-principles mindset and genuine curiosity about sound.

Responsibilities

• Own data-to-production workflow for audio models.
• Train models, build datasets, and conduct listening tests.
• Investigate failures and ship improvements to real users.

Skills

Speech models

Audio processing

Model deployment

Evaluation methods

Data pipelines

Education

Bachelor's degree in a relevant field

Tools

Python

PyTorch

TensorFlow

Experiment tracking

Job description

We're a team of audio experts solving one of the hardest problems in speech technology.

Real conversations are messy. People interrupt each other, move around, sit at different distances from the microphone, and speak in rooms filled with reverb and background noise.

Most models are trained on synthetic mixtures because high-quality paired recordings of real conversations are extremely difficult to collect. Models that perform well in simulated conditions often struggle when they meet the real world.

The solution:

We've built technology that captures both the room and the individual speakers at the same time.

This has given us something rare: a proprietary dataset of real conversations with the reference signals needed to train and evaluate a new generation of speech enhancement, separation, dereverberation, and spatial audio models.

The Role

We're looking for an exceptional Applied ML Engineer to turn this advantage into audio improvements people can immediately hear.

You'll own the journey from data to production building datasets and listening tests, training models, investigating failures, and shipping improvements to real users.

A few things we're looking for:

• Hands-on experience training speech or audio models
• Experience with real-world, imperfect audio not only benchmark datasets
• A solid understanding of evaluation, including perceptual listening tests
• The ability to take models from experimentation into production
• A first-principles mindset and genuine curiosity about sound

Experience with speech enhancement, source separation, dereverberation, room acoustics, microphone arrays, ambisonics, or spatial audio would be particularly valuable.