Machine Learning Engineer – Audio & Signals

19196
1 Mar, 2026 to 31 Oct, 2026
Stockholm (Onsite)

We are looking for a hands-on Machine Learning Engineer to join a Stockholm-based team working with advanced audio and signal-based data. The role focuses on deep technical work, experimentation, and delivering production-grade ML solutions.


You will take end-to-end ownership of technical problems — from exploration and prototyping to scalable, reliable systems in production.


About the role

You will work with machine learning and AI methods applied to complex sound and signal data, combining statistical modeling, deep learning, and experimentation. The role centers on discovering patterns in audio, designing robust models, and transforming experimental ideas into well-engineered solutions.


This position suits someone who thrives in a role with strong technical focus and end-to-end responsibility.


What you’ll do

Design, train, and iterate on machine learning models for audio and signal analysis

Own feature extraction and modeling of structured attributes from sound data

Build and maintain internal ML pipelines, tools, and experimentation workflows

Explore and evaluate advanced representations and embeddings for audio signals

Apply and evaluate AI-driven approaches such as deep learning, representation learning, and data-driven modeling for audio and signal-based problems

Collaborate closely with backend engineers and domain specialists to integrate ML models into production systems

Contribute directly to ML architecture, evaluation strategies, and system reliability

Continuously test, refine, and improve models based on data and real-world usage


We think you’ll thrive if you

Are highly proficient in Python and comfortable with modern ML frameworks such as PyTorch

Enjoy writing clean, efficient, and maintainable code and scaling experiments into production

Have experience with data pipelines and an interest in data engineering

Are curious about structured systems underlying sound (timing, segmentation, organization, theory-driven models)

Have hands-on experience with or strong interest in signal processing

Are comfortable working with audio and/or image data

Have experience applying AI techniques and machine learning models to real-world data problems

Prefer owning problems end-to-end

Enjoy close collaboration while staying deeply technical


Additional strengths (nice to have)

Background in audio analysis, MIR, or related applied research

Practical experience with DSP


Technical environment

Backend: Python, FastAPI

Machine Learning: Python, PyTorch

Data: PostgreSQL

Cloud: AWS

Mobile: Flutter

Frontend: Angular (TypeScript)


Location: Stockholm