Senior Engineering Manager, Applied AI & Audio

About this role

Join our global force of 400+ innovators, blending the latest in tech with the greatest in soundtracking, from our Stockholm HQ to offices in London, New York, Los Angeles, Berlin, Paris, Oslo, and Seoul. We’re an industry leader with a startup mentality. We take what we do seriously, but we don’t take ourselves too seriously. Creating and collaborating to transform the sound of streaming, content, and culture. Come join us, and let the world feel your work We are looking for a Senior Engineering Manager to lead our Sound Solutions team, focused on using AI to intelligently adapt music for real-world creative use.

This is an impactful leadership role where you will be accountable for people, delivery, and technical direction, shipping and scaling AI-powered music adaptation capabilities that make a real difference to how creators experience sound. You’ll lead a team of Machine Learning Engineers and Software Engineers who own both the models and the backend services that put them in creators’ hands. You’ll work with a Product Manager and alongside data platform, MLOps, and product engineering teams.

What you'll do - Lead the team in taking AI models from training to live production, including real-time inference, and keep them reliable as usage grows. - Own the team’s technical direction and prioritization, balancing model quality, speed, and cost, in partnership with your PM. - Shape the team’s model strategy with your ML engineers: evaluate models, open-source solutions, and vendor offerings, and decide what is worth pursuing.

- Build a high-ownership team culture through mentorship and honest feedback, growing engineers across ML and software. - Partner with data, MLOps, and product engineering teams to integrate our AI into the creator experience, including the backend services behind it. What we're looking for - Significant engineering leadership experience, with a track record of managing and growing teams across ML and software engineering that ship ML systems into production.

- A solid practical understanding of the ML lifecycle (training, evaluation, deployment) and the current AI landscape, including fine-tuning on domain-specific data. You don’t need to be the most experienced ML engineer on the team, but you can follow the technical details, ask sharp questions, and evaluate models critically. - You’ve built and shipped production software, ideally ML-powered services, and can credibly lead engineers who build backend services.