spotify - Machine Learning Engineering Manager - Music
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Requirements
• You have experience leading or managing engineering teams, ideally in machine learning or research-focused environments • You are experienced with machine learning systems and frameworks such as PyTorch, TensorFlow, or JAX • You understand distributed systems and have worked with large-scale training or compute environments • You communicate clearly and collaborate effectively across research and engineering disciplines • You have partnered with product and cross-functional teams to deliver meaningful outcomes • You have delivered complex technical projects and can balance experimentation with reliability • You care about building inclusive teams and supporting engineers at different stages of their careers • You have an interest in music technology, audio, or generative AI • ## Where You'll Be • We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location (excluding France due to on-call restrictions). • This team operates within the Central European and GMT time zone for collaboration. • This team collaborates across regions, with core working hours typically overlapping between CET 3pm–6pm and EST 9am–12pm. • At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Responsibilities
• Lead and support a team of research engineers and machine learning engineers, helping them grow and do their best work • Define and deliver the engineering roadmap, balancing fast experimentation with production readiness • Build and scale machine learning infrastructure, including training pipelines, experimentation systems, and shared tooling • Partner with research scientists, product managers, and platform teams to bring ideas from research into user-facing features • Improve engineering practices in a research environment, including testing, reproducibility, and deployment workflows • Support the transition of research prototypes into reliable, scalable production systems • Contribute to strategic planning and align engineering work with broader company priorities
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