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Jobs/Machine Learning Engineer Role/spotify - Senior Machine Learning Engineer - Personalization
spotify

spotify - Senior Machine Learning Engineer - Personalization

London / Stockholm1w ago
RemoteSeniorEMEAArtificial IntelligenceMachine Learning Engineer

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Requirements

• You have hands-on experience building recommendation or personalization systems at scale • You’re comfortable working across the ML stack, including pipelines, backend systems, and infrastructure • You think in products and understand how model decisions impact user experience • You’re fluent with AI-assisted development and use it to accelerate experimentation thoughtfully • You’re curious about emerging approaches like generative models and agentic ML systems • You’ve taken models from prototype to production and care about reliability and monitoring • You’re comfortable with ambiguity and enjoy defining new approaches in evolving problem spaces • ## Where You'll Be • This role is based in London • We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home. • 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

• Design, train, and ship machine learning models that power recommendations on the Now Playing View for hundreds of millions of users • Own ranking systems end-to-end, from experimentation and training pipelines to online serving and monitoring • Build and iterate on generative and agentic ML approaches to improve session steering and cross-content discovery • Work in an AI-native development environment, using AI tools to accelerate development while applying strong engineering judgment • Run A/B experiments, define success metrics, and translate improvements into measurable user impact • Collaborate closely with engineers, data scientists, researchers, and product managers to bring ideas into production • Shape the ML roadmap by identifying high-impact opportunities and mentor teammates

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