Machine Learning Engineer - Artist-First AI Music Lab

What You’ll Do

• Design, build, evaluate, and improve machine learning training and inference pipelines that power new AI-driven music experiences and help take them to fully scaled production-ready features.
• Apply machine learning and prompt engineering knowledge across complex ML pipelines to support rich user experiences involving large language models.
• Create evaluation frameworks, including LLM-as-judge pipelines, to measure quality and build fast feedback loops that enable rapid and confident iteration.
• Partner with music subject-matter experts to bootstrap training and reference data, including synthetic generation, expert curation, and taxonomy design.
• Build scalable systems that balance experimentation velocity with production rigor, ensuring strong performance, reliability, and latency at Spotify scale.
• Collaborate closely with Data Science teams to connect evaluation frameworks with real-world usage signals and continuously improve model quality.
• Contribute to technical direction and engineering best practices across model deployment, observability, experimentation, and production infrastructure.
• Collaborate cross-functionally with engineering, product, design, and music industry partners to shape entirely new listening experiences for artists and fans.

Who You Are

• Experienced in applying machine learning in production environments.
• Hands-on experience working with large language models, prompt engineering, evaluation systems, and shipping LLM-driven features in production.
• Experience building and maintaining production ML systems using Python, Java, Scala, or similar languages.
• Experience with building large-scale data pipelines for sourcing, preparing, and evaluating training data.
• Experience with cloud platforms such as GCP, AWS, Azure, or similar infrastructure environments.
• Comfortable explaining machine learning concepts, assumptions, and trade-offs to both technical and non-technical audiences.
• Experience building user-facing products and strong judgment around conversational AI and generative user experiences.
• Care deeply about experimentation, iteration, and using data to guide product and engineering decisions.
• Thrives in collaborative, cross-functional teams that move quickly, experiment often, and continuously learn.