Staff Machine Learning Engineer, GenRecs | Life at Spotify

Spotify

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Salary
$215,136 - $307,337

Job Description

Job Overview

As a Staff Machine Learning Engineer in the GenRecs team at Spotify, you will play a pivotal role in enhancing the way users discover music and podcasts through personalized recommendations. This role focuses on leveraging generative recommenders and foundational user modeling to build innovative product features that connect fans and artists. You will work with a cross-functional team to define and execute the machine learning strategy, ensuring scalability and high-quality solutions that cater to millions of users.

Technical Requirements

Required Skills
  • • machine learning
  • • recommender systems
  • • Java
  • • Scala
  • • Python
Preferred Skills
  • • PyTorch
  • • Tensorflow
  • • JAX
Experience Level

Strong background with production experience in large-scale machine learning systems

Responsibilities

  • • Define the machine learning technical strategy for generative recommenders and user modeling
  • • Collaborate with cross-functional teams to build personalized product features
  • • Provide technical leadership to accelerate development and ensure scalability
  • • Design, build, evaluate, and refine Spotify's personalization products
  • • Promote best practices in ML model development and testing
  • • Engage with the ML community and stay updated on research trends

Technical Environment

Languages
Frameworks

Benefits & Perks

  • • Extensive learning opportunities
  • • Flexible share incentives
  • • Global parental leave (six months)
  • • Employee assistance program
  • • Flexible public holidays
  • • Health insurance
  • • 401(k) retirement plan
  • • Monthly meal allowance
  • • 23 paid days off
  • • 13 paid flexible holidays
  • • Paid sick leave

Additional Information

Location
New York, with flexibility within the North America region
Type
Permanent
Compensation
$215,136 - $307,337 plus equity

About Spotify

Spotify is a digital music service that gives you access to millions of songs.

Company Size
unknown
Categories
Consumer Web digital Hospitality Hospitality & Travel music Music, Audio & Entertainment Music Streaming streaming