Engineering Manager
Stockholm, Sweden
73,000–93,000 SEK per month benefits
Hybrid – 3 days office / 2 days remote
Full-time
Most ML leadership jobs pull you away from the models. This one puts you in charge of them.
You will lead the generative audio systems that create music and sound effects for a global content platform used by millions of creators. The models already exist. The research direction is clear. What is needed now is someone who can own the entire system and push it into production at scale.
You will guide how large diffusion models for music are trained, evaluated and deployed. Your decisions determine how these models evolve technically and how they run in real products where latency, stability and cost matter.
What you will build
You will help build systems that automatically adapt music to video, generate sound effects directly from visual input, and allow creators to produce soundtracks in seconds.
A small team of five PhD educated ML engineers and a contractor will rely on your technical direction while you shape how the technology moves from experimentation into production.
You will work across the full machine learning lifecycle. Training large generative models. Defining evaluation strategies. Making architectural decisions about inference, optimisation and deployment. Working closely with platform and MLOps engineers to ensure the systems run reliably in production.
Why this environment is different
The models are trained on a proprietary catalogue of licensed music and structured datasets created through a global network of artists who produce and remix tracks specifically for training. This produces a dataset most AI labs simply do not have.
You will also work close to the research frontier, with collaborations involving groups connected to unicorn start up labs and tier 1 universities.
The result is rare: frontier generative model work inside a stable, profitable company where the technology actually ships to users.
What you bring
Deep experience training large machine learning models.
Experience with generative models such as diffusion, audio models, vision models or large language models.
Strong ML system design skills across training, evaluation and production deployment.
Comfort guiding engineers and making architectural decisions that shape how ML systems evolve.
Experience shipping ML systems where latency, reliability and cost matter.
Team and setup
You will lead a team of five PhD educated engineers and one contractor working on generative audio systems.
The team works closely with platform engineering, data infrastructure and MLOps to ensure models move from experimentation into production features.
Curious?
If you have trained large generative models before and want ownership of the entire system rather than a narrow piece of it, this will likely be interesting.
Send a message / apply, and I can share more context.
