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136,000+ live jobsSenior Machine Learning Engineer - Messaging Platform
Hybrid in London·Added 5 months ago (last checked 8 Oct)
Overview
Job details
Permanent contract
Per the ad.
Hybrid
Office and home days — the ad has the split.
Requirements
Be near London for hybrid days
No relocation package mentioned.
Skills
Requirements
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
The role
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.
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
About Spotify
- Industry
- Music/Audio
- Founded
- 2006
- Employees
- 5,000–10,000
- Headquarters
- Stockholm, Sweden
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