Machine Learning Engineer - Artist-First AI Music Lab

We are seeking a Machine Learning Engineer to join our Artist-First AI Music lab. Our team designs and builds state-of-the-art generative products for music that create breakthrough experiences for fans and artists. We invent entirely new listening experiences that center and celebrate artists and creatives. All of our products will put artists and songwriters first, through these four principles:

Partnerships with record labels, distributors, and music publishers: We’ll develop new products for artists and fans through upfront agreements, not by asking for forgiveness later.

Choice in participation: We recognize there’s a wide range of views on use of generative music tools within the artistic community. Therefore, artists and rightsholders will choose if and how to participate to ensure the use of AI tools aligns with the values of the people behind the music.

Fair compensation and new revenue: We will build products that create wholly new revenue streams for rightsholders, artists, and songwriters, ensuring they are properly compensated for uses of their work and transparently credited for their contributions.

Artist-fan connection: AI tools we develop will not replace human artistry. They will give artists new ways to be creative and connect with fans. We will leverage our role as the place where more than 700 million people already come to listen to music every month to ensure that generative AI deepens artist-fan connections.

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.
• Work 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.
• 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.

Originally posted on Himalayas