Senior Machine Learning Engineer - Artist-First AI Music Lab
Spotify Boston, Massachusetts, United States · $184K–$263K/yr
Musicians · 5,001-10,000 employees
About the role
Design, build, and improve machine learning training and inference pipelines for generative music products. Collaborate with cross-functional teams to integrate evaluation frameworks and ensure production-ready system performance.
What they look for
Requirements
Requires extensive experience in production machine learning, specifically with large language models and prompt engineering. Proficiency in Python, Java, or Scala and experience with cloud infrastructure are essential for this role.
Benefits
Full description
We are seeking a Senior 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 rights-holders 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.
\n
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.
\nThe United States base range for this position is $184,049 - $262,928.00 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.
Similar roles
-
Machine Learning Engineer
Lyft New York, New York, United States · $141K–$176K/yr
-
Systems Engineering Intern - Machine Learning Expert
Texas Instruments Dallas, Texas, United States
-
Associate, AI & Machine Learning Intern
Pearson Bangalore, Karnataka, India
-
Consultant Senior - Data Science, Machine Learning & GenAI
Delta Consulting Company Luxembourg, Luxembourg
-
Machine Learning Engineer (Remote or Relocation to Montenegro)
Libertex Group Montenegro
-
Machine Learning Engineer – Document Digitization (LLMs)-Vice President
JPMorgan Chase & Co. Jersey City, New Jersey, United States · $138K–$185K/yr