Senior Machine Learning Engineer - Personalization

What You'll Do

• Contribute to the design, development, evaluation, and iteration of recommendation models — including candidate generation, ranking, and embedding models — powering music surfaces at scale.
• Drive hands-on ML development to improve reward signals and recommendation quality across Home, Now Playing, and other core surfaces.
• Contribute to the team's adoption of generative recommendation models, partnering with ML and AI infrastructure teams.
• Promote best practices in ML systems development, testing, and experimentation within the team.
• Collaborate with Data Science, Product, and Design partners to define success metrics, run A/B experiments, and translate insights into product improvements.
• Partner with teams across Personalization to integrate and test new signals in recommendation systems.

Who You Are

• You have a strong background in machine learning and enjoy applying theory to real-world applications, with expertise in statistics and optimization — particularly sequential models, transformers, generative AI, and LLMs.
• You have hands-on experience building and shipping production machine learning systems at scale, ideally in personalization or recommendation systems.
• You have experience implementing ML systems in Java, Scala, Python, or similar languages. Familiarity with PyTorch, Ray or Hugging Face is a plus.
• You have some experience with large-scale distributed data processing frameworks such as Apache Beam, Apache Spark, or Scio, and cloud platforms like GCP or AWS.
• You have experience collaborating across teams on complex ML projects and navigating cross-functional stakeholders.
• You care about agile software processes, data-driven development, reliability, and disciplined experimentation.

Where You'll Be

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