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

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