The opportunity
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…
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
What they're looking for
- 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
