Senior Machine Learning Engineer

Spotify•New York, NY

About The Position

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, weâre behind some of Spotifyâs most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and youâll keep millions of users listening by making great recommendations to each and every one of them. We are looking for a Machine Learning Engineer to join the Personalization team - an area of hardworking engineers that are passionate about understanding what drives user satisfaction with Spotify - and who make impactful changes to Home recommendation systems to achieve this goal. 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.

Requirements

You have a strong background in machine learning, enjoy applying theory to develop real-world applications, with expertise in statistics and optimization, especially in sequential models, transformers, generative AI and large language models, and relevant fine-tuning processes.
You have hands-on experience with large cross-collaborative machine learning projects and managing stakeholders.
You have hands-on experience implementing production machine learning systems at scale in Java, Scala, Python, or similar languages.
Experience with PyTorch, Ray, Hugging Face and related tools is required.
You have some experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, or even our open source API for it - Scio, and cloud platforms like GCP or AWS.
You care about agile software processes, data-driven development, reliability, and disciplined experimentation.

Responsibilities

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 systems.
Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization.