Description:

The Personalization team at Spotify aims to enhance the listening experience by providing tailored recommendations for music and podcasts.
The Staff Machine Learning Engineer will focus on recommender systems that integrate generative recommenders and user taste understanding.
This role involves defining and executing the machine learning technical strategy for product areas, contributing to the development of Spotify's content and user representations.
Responsibilities include collaborating with a cross-functional team to build new product features, providing technical leadership, and promoting best practices in ML model development.
The engineer will engage with the broader ML community and stay updated on ML research to inspire and evolve Spotify's approaches.

Requirements:

A strong background in machine learning and recommender systems is essential, with the ability to connect research to end-user impact.
Production experience in developing large-scale machine learning systems using Java, Scala, Python, or similar languages is required; familiarity with PyTorch, Tensorflow, or JAX is a plus.
Hands-on experience with training and operating transformer models in production settings is preferred.
The candidate should be comfortable leading projects from inception to completion and managing ambiguity in high-impact projects.
Strong communication skills and the ability to influence technical and product stakeholders are necessary.
A commitment to agile software processes, data-driven development, and disciplined experimentation is expected.
Staying current on ML trends and a passion for enhancing the listening experience for users globally are important.

The position offers a competitive salary range of $215,136 to $307,337, plus equity.
Benefits include health insurance, six months of paid parental leave, a 401(k) retirement plan, and a monthly meal allowance.
Employees receive 23 paid days off and 13 paid flexible holidays, along with paid sick leave.
Spotify promotes inclusivity and provides reasonable accommodations during the recruitment process to support all candidates.