{"id":118102,"url":"https://alion.io/job/spotify-senior-machine-learning-engineer-messaging-platform-2","title":"Senior Machine Learning Engineer - Messaging Platform","company":{"id":57,"name":"Spotify","domain":"spotify.com","url":"https://alion.io/company/spotify","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":83,"open_postings":13,"ghost_share":0,"stale_share":0.615,"repost_share":0.077,"time_to_fill_p50_days":50,"computed_at":"2026-11-07T05:47:08Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom","Stockholm, Sweden"],"countries":["GB","SE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":122000,"max_usd":217000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":49},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false}],"status":"live","first_seen_at":"2026-05-08T14:29:46Z","employer_posted_date":"2026-05-08","last_verified_at":"2026-11-07T22:48:36Z","board_verified":true,"closed_at":null,"days_open":183,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":182},"description":"Spotify’s Subscriptions Mission focuses on converting listeners into lifelong subscribers by delivering seamless, valuable experiences across pricing, packaging, and customer journeys. We build the systems and tools that power acquisition, retention, and overall subscription growth at scale.\nThe 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.\nWe’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.\nWhat You'll Do\nDesign, build, and ship machine learning models that optimize messaging across push, email, and in-app channels\nPlan and run A/B experiments in a multi-objective environment, balancing conversion, engagement, retention, and reachability\nContribute to reinforcement learning systems that optimize for long-term user outcomes rather than immediate interactions\nPartner with product managers, data scientists, and engineers to define what success looks like and how to measure it\nOwn the full ML lifecycle, from data and modeling to deployment, monitoring, and iteration\nIntegrate ML models with upstream systems, including domain value signals and opportunity generation frameworks\nHelp shape the future of AI-assisted development within the team, exploring how tools can accelerate experimentation and delivery\nWho You Are\nYou have strong experience building and deploying machine learning models in production environments at scale\nYou are comfortable translating business problems into ML solutions and discussing trade-offs with cross-functional partners\nYou have worked on complex optimization problems such as ranking systems or multi-objective decision-making\nYou bring hands-on experience with PyTorch and distributed systems such as Ray or similar frameworks\nYou understand experimentation deeply and can design reliable tests in environments with interacting metrics\nYou are able to analyze results using approaches like causal inference or metric decomposition when needed\nYou have experience with or curiosity about reinforcement learning and long-term optimization systems\nYou enjoy working across disciplines and navigating ambiguity while shaping strategy and direction\nWhere You'll Be\nThis role is based in London and Stockholm\nWe offer you the flexibility to work where you work best! 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