Backend Engineer – Personalization

Role Overview

• Own the backend systems that rank and serve personalized content to hundreds of millions of Spotify users on the Now Playing View, one of Spotify's most visible surfaces.
• Work in an AI-native development workflow. To implement backend changes across services, review their output, and ensure architectural coherence across the system.
• Integrate with ML serving infrastructure and collaborate with ML engineers to bring models from training to production within tight latency constraints.
• Make product-informed backend decisions to understand why a feature matters to users, not just how to implement it. Connecting system design choices to scroll engagement, discovery, and user experience.
• Design the patterns, tests, and guardrails that keep agent-generated code consistent, safe, and production-ready across services.
• Run and analyze A/B experiments from instrumenting new features to interpreting rollout metrics. Shipping what the data says works.
• Own production reliability for your services including monitoring, alerting, on-call, and incident response. Collaborate as part of a cross functional team including machine learning engineers, data scientists, research scientists, and product managers, to turn ideas into production systems.

Requirements

• You are experienced in building and operating backend services in Java or a JVM language within a microservice architecture.
• You think in systems, not just code. You reason about service contracts, failure modes, and architectural tradeoffs before jumping to implementation. When reviewing code, whether written by a person or an AI agent, you catch subtle issues in correctness, performance, and design coherence.
• You're fluent with AI-assisted development. You have hands on experience working with tools like Claude Code to orchestrate work. Decomposing problems into well-scoped tasks, directing multiple parallel agent streams, evaluating their output critically, and iterating. You see AI agents as a force multiplier, not a crutch.
• You're product-oriented with an understanding of the user problem and understanding of why backend change matters in terms of user engagement, not just system metrics, energized by working on a surface that hundreds of millions of people interact with daily.
• You set up guardrails, not just features. You care about designing test suites, architectural patterns, and code conventions that make it safe for agents (and teammates) to move fast. Thinking about what makes a codebase reviewable and verifiable.
• You're excited to work embedded in an ML team, you won't train models yourself, but you'll partner closely with ML engineers to build and maintain the serving infrastructure, feature pipelines, and backend systems that bring their models to production.
• You have practical experience with system design and can reason about tradeoffs in latency, reliability, and scalability across distributed services.
• You care about production ownership including on-call, monitoring, and operational health feel like natural parts of the job, especially when you're responsible for code you may have reviewed rather than written line-by-line.
• You're comfortable with ambiguity. AI-native workflows are new; you're the kind of engineer who figures out best practices rather than waiting for them to be documented.

Tech Stack

• Java

Benefits

• Flexible work arrangements