Senior Machine Learning Engineer, Personalization

Build and improve machine learning models that make Spotify Search more relevant, personalized, and engaging for listeners.

Develop and optimize ranking and backend retrieval systems that help listeners discover music, videos, audiobooks, and other content.

Explore emerging AI and agentic approaches to create more intuitive and conversational Search experiences.

Design, implement, and evaluate machine learning solutions, taking ideas from early experimentation through production.

Partner with engineers, product managers, designers, and researchers to identify opportunities and deliver meaningful improvements to Search.

Use experimentation, data analysis, and performance metrics to understand model behavior, validate hypotheses, and improve the listener experience.

Contribute to scalable machine learning infrastructure and engineering practices that support reliable, high-quality Search experiences.

Share knowledge, challenge assumptions constructively, and help shape the technical direction of machine learning within the squad.

You have experience building, deploying, and improving machine learning systems in production environments.

You have a strong understanding of machine learning approaches for information retrieval, search ranking, recommendation systems, or personalization.

You are proficient in Python and comfortable working with modern machine learning frameworks and data processing tools.

You have experience and a desire to lean into backend engineering at Spotify scale.

You understand how to design and evaluate experiments, interpret model performance, and translate technical findings into product improvements.

You have experience working with large-scale datasets and understand the challenges of building reliable, scalable machine learning systems.

You are curious about emerging AI technologies, including large language models and agentic systems, and interested in their potential to improve discovery experiences.

You enjoy working across disciplines, communicating technical ideas clearly, and learning from different perspectives.

You are comfortable navigating ambiguity, taking ownership of complex challenges, and balancing experimentation with practical delivery.

This role is based in Eastern Standard Time

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.