Senior Machine Learning Engineer - Messaging Platform

The Messaging Platform powers Spotify’s communications to over a billion users — from push notifications to emails and in-app messages that connect listeners to the content they love. Within this space, the Paloma squad focuses on message optimization: deciding which message reaches which user, through which channel, and at what moment.

We’re evolving how messaging works at Spotify — moving from short-term optimization toward systems that understand long-term user journeys. By combining reinforcement learning approaches with deeper domain signals, we’re expanding how machine learning shapes the entire messaging funnel.

What You'll Do

• Design, build, and ship machine learning models that optimize messaging across push, email, and in-app channels
• Plan and run A/B experiments in a multi-objective environment, balancing conversion, engagement, retention, and reachability
• Contribute to reinforcement learning systems that optimize for long-term user outcomes rather than immediate interactions
• Partner with product managers, data scientists, and engineers to define what success looks like and how to measure it
• Own the full ML lifecycle, from data and modeling to deployment, monitoring, and iteration
• Integrate ML models with upstream systems, including domain value signals and opportunity generation frameworks
• Help shape the future of AI-assisted development within the team, exploring how tools can accelerate experimentation and delivery

Who You Are

• You have strong experience building and deploying machine learning models in production environments at scale
• You are comfortable translating business problems into ML solutions and discussing trade-offs with cross-functional partners
• You have worked on complex optimization problems such as ranking systems or multi-objective decision-making
• You bring hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks
• You understand experimentation deeply and can design reliable tests in environments with interacting metrics
• You are able to analyze results using approaches like causal inference or metric decomposition when needed
• You have experience with or curiosity about reinforcement learning and long-term optimization systems
• You enjoy working across disciplines and navigating ambiguity while shaping strategy and direction

 

Where You'll Be

• This role is based in London and Stockholm
• 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