Senior Machine Learning Engineer - Messaging Platform

3 мес. назад

United KingdomEuropeSeniorHybrid

machine learningreinforcement learningdata analysis

Senior role focused on machine learning for message optimization in 's messaging platform.

’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. The Messaging Platform powers ’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 — moving from short-term optimization toward systems that understand long-term user journeys. By combining reinforcement l

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

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

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