Senior Backend Engineer - Surfaces/PZN

Spotify•New York, NY

3d•Remote

About The Position

The Personalization team at Spotify focuses on enhancing the listener experience by making decisions about what to play next more enjoyable. They are responsible for beloved features like Blend and Discover Weekly, built on a deep understanding of music and podcasts. The Experimentation and Learning team (SEAL), within the Surfaces Foundation product area, builds the infrastructure for testing, evaluating, and shipping personalized experiences across Spotify Surfaces, including Home. They aim to create a unified, agent-driven experimentation ecosystem that allows teams to set up and run experiments rapidly.

Requirements

Experience building and operating large-scale backend systems serving high-traffic user experiences.
Comfortable using AI-assisted tools to develop high quality software.
Experienced with distributed systems, APIs, and service-oriented architecture.
Knowledge of how to design systems with performance, scalability, and reliability in mind.
Product-minded engineer who thrives at the intersection of infrastructure, AI, and experimentation.
Experience translating conceptual architectures into scalable, production-ready code.
Comfortable working across multiple teams and collaborating with cross-functional partners.
Care about creating maintainable systems that enable fast iteration and experimentation.
Motivated by turning fragmented, manual processes into elegant, automated workflows.
Experience defining and working with SLOs, SLAs, and observability tooling.

Nice To Haves

Experience with LLMs, agent frameworks, or A/B testing infrastructure is a bonus.

Responsibilities

Own and evolve the core experimentation service, building agentic workflows that automate experiment setup, configuration, and validation across multiple backend systems.
Design and maintain API-first orchestration layers that coordinate across experimentation and content delivery systems.
Build and integrate LLM-driven workflows into robust, production-ready infrastructure, including planner, generator, and validator architectural patterns.
Define and uphold SLOs and SLAs for experimentation infrastructure, contributing to a strong culture of reliability and operational excellence.
Partner closely with content, feature, and platform teams to scale automated experimentation patterns across a growing range of surfaces and configurations.
Identify, diagnose, and resolve bottlenecks in experiment orchestration to ensure fast and reliable workflows.
Collaborate across squads to continuously improve how experimentation and evaluation are delivered across Spotify Surfaces.
Contribute to architectural decisions that improve system scalability, observability, and maintainability.