Senior Machine Learning Engineer - Messaging Platform

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.   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.   country: GB all locations: [London Stockholm] commitment: Permanent department: Engineering location: London team: Subscriptions R&D 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