Spotify

Machine Learning Engineer- Artist-First AI Music Lab

Rol remoto de Engineering con fit claro de ubicación del candidato.

Publicado18 jul 2026

Países elegibles1 país aceptado

Señal de seniorityMiddle

Modelo de trabajoRemoto

Ubicaciones aceptadas para candidatos

Estados Unidos

Resumen del rol

Contenido del rol extraído en secciones para revisar más rápido.

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.
Work cross-functionally with engineering, product, design, and music industry partners to shape entirely new listening experiences for artists and fans.

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

We offer you the flexibility to work where you work best! For this role, you can be within the Eastern United States region as long as we have a work location.
This team operates within the EST time zone for collaboration.

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