Spotify - Machine Learning Engineer - Artist-First AI Music Lab
Requirements
• 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. • Where You’ll Be • 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. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Find our AI notice here: https://lifeatspotify.com/ai-notice
Responsibilities
• 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. • 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.
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