# Senior Machine Learning Engineer - Messaging Platform

**Company:** [Spotify](https://hotfix.jobs/companies/spotify)
**Location:** London, United Kingdom, Stockholm, Sweden
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** Machine Learning, PyTorch, Ray, Reinforcement Learning, A/B Testing, Causal Inference, Metric Decomposition, Ranking Systems, Distributed Systems, Model Deployment, Model Monitoring
**Posted:** 2026-05-08

> Build and operate production machine learning systems that optimize Spotify messaging across channels and user journeys. The role emphasizes ranking, experimentation, reinforcement learning, long-term optimization, and collaboration across product and engineering teams.

## Job Description

## 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 success criteria and measurement approaches.
- 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.
- Explore AI-assisted development tools to accelerate experimentation and delivery.

## Requirements
- Strong experience building and deploying machine learning models in production environments at scale.
- Ability to translate business problems into ML solutions and discuss trade-offs with cross-functional partners.
- Experience with 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.
- Deep understanding of experimentation and reliable testing in environments with interacting metrics.
- Ability to analyze results using approaches such as causal inference or metric decomposition.
- Experience with, or curiosity about, reinforcement learning and long-term optimization systems.
- Ability to work across disciplines, navigate ambiguity, and shape strategy and direction.

## Work Arrangement
- Based in London and Stockholm.
- Hybrid flexibility with some in-person meetings and the option to work from home.

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