Machine Learning Engineer, Artist-First AI Music Lab

This is a fully remote position, open to applicants in New York.

📋 Description

• Design, construct, assess, and enhance machine learning training and inference pipelines that empower innovative AI-driven music experiences, transitioning them to fully scalable production-ready features.

• Utilize expertise in machine learning and prompt engineering within complex ML pipelines to foster rich user experiences that leverage large language models.

• Develop evaluation frameworks, including LLM-as-judge pipelines, to assess quality and create rapid feedback loops that facilitate swift and confident iteration.

• Collaborate with music subject-matter experts to initiate training and reference data, incorporating synthetic generation, expert curation, and taxonomy design.

• Create scalable systems that strike a balance between experimentation speed and production standards, ensuring robust performance, reliability, and low latency at Spotify’s scale.

• Work closely with Data Science teams to link evaluation frameworks with real-world usage signals, continuously enhancing model quality.

• Play a role in shaping the technical direction and engineering best practices concerning model deployment, observability, experimentation, and production infrastructure.

• Engage cross-functionally with engineering, product, design, and music industry partners to develop entirely new listening experiences for both artists and fans.

⛳️ Requirements

• Proven experience in applying machine learning within production settings.

• Hands-on experience with large language models, prompt engineering, evaluation systems, and deploying LLM-driven features in production.

• Proficiency in building and maintaining production ML systems using Python, Java, Scala, or similar programming languages.

• Experience in constructing large-scale data pipelines for sourcing, preparing, and assessing training data.

• Familiarity with cloud platforms such as GCP, AWS, Azure, or other similar infrastructure environments.

• Ability to clearly explain machine learning concepts, assumptions, and trade-offs to both technical and non-technical audiences.

• Experience in developing user-facing products and a strong understanding of conversational AI and generative user experiences.

• A strong commitment to experimentation, iteration, and leveraging data to inform product and engineering decisions.

• A preference for working in collaborative, cross-functional teams that prioritize speed, frequent experimentation, and continuous learning.

🏝️ Benefits

• Health insurance

• Six months of paid parental leave

• 401(k) retirement plan

• Monthly meal allowance

• 23 paid days off

• 13 paid flexible holidays

• Paid sick leave