Senior Machine Learning Engineer, Personalization, Rewards

The Rewards team in Personalization (PZN) is defining the next generation of large-scale personalization at Spotify by pioneering novel Reinforcement Learning (RL) methods for Large Language Models (LLMs). We drive massive impact across all recommendations discovery surfaces by moving beyond simple clicks to model and optimize for true long-term user satisfaction. Our core mission is to bridge discovery with lasting listening habits by developing and deploying sophisticated, mid-term behavioral reward signals—such as retention and habit formation metrics—that directly shape the LLM-powered recommendation experience.

We are looking for a Machine Learning Engineer to make impactful changes to our recommendations and discovery algorithms. As an integral part of the squad, you will collaborate with research scientists, data scientists, and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction. 

What You'll Do

Contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development.

Lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation and discovery systems.

Promote and role-model best practices of ML systems development, testing, and evaluation throughout the organization.

In your first 6 months, you will be responsible for ML development: prototyping models, building pipelines, productizing/scaling models, and launching A/B tests for personalized generative recommendations at Spotify.

Who You Are

You have a strong background in machine learning and enjoy applying theory to develop real-world applications.

Reinforcement Learning (RL) expertise is key, and experience in RL for recommendations is a must have.

Expertise in statistics and optimization, especially in sequential models, transformers, generative AI, large language models (LLMs are a plus), and relevant fine-tuning processes.

Where You'll Be