# Machine Learning Engineer - Messaging Platform **Company**: Spotify **Location**: London **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Technology **Apply**: https://jobs.lever.co/spotify/c322d068-5b59-4658-b618-bb2a032eeb9b?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_52f4684b-0f3 ## Description Spotify's Subscriptions Mission focuses on converting listeners into lifelong subscribers by delivering seamless, valuable experiences across pricing, packaging, and customer journeys. 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. ## Responsibilities - 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 ## Requirements - Strong experience building and deploying machine learning models in production environments at scale - Comfortable translating business problems into ML solutions and discussing trade-offs with cross-functional partners - Worked on complex optimization problems such as ranking systems or multi-objective decision-making - Hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks - Understand experimentation deeply and can design reliable tests in environments with interacting metrics - Analyze results using approaches like causal inference or metric decomposition when needed - Experience with or curiosity about reinforcement learning and long-term optimization systems - 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 ## Skills ### Required - PyTorch - Distributed Systems - Reinforcement Learning - Machine Learning - Experimentation --- Source: [Apply at jobs.lever.co](https://jobs.lever.co/spotify/c322d068-5b59-4658-b618-bb2a032eeb9b?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)