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职位Spotify

Machine Learning Engineer, Personalization

Spotify

Machine Learning Engineer, Personalization

Spotify

New York, NY

·

On-site

·

Full-time

·

1d ago

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.

Samba sits at the heart of Spotify’s personalization engine, powering experiences like autoplay, radio, and personalized mixes. We work on complex sequencing and optimization problems—balancing what users love with how Spotify supports creators and the business.

Our team blends machine learning, backend engineering, and data expertise, and collaborates across North America and Europe to deliver impactful, real-time personalization at scale.

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关于Spotify

Spotify

Spotify

Public

Spotify is a Swedish audio streaming and media service provider founded in April 2006 by Daniel Ek and Martin Lorentzon. As of December 2025, it was one of the largest providers of music streaming services, with over 751 million monthly active users comprising 290 million paying subscribers.

5,001-10,000

员工数

Stockholm

总部位置

$23B

企业估值

评价

4.1

10条评价

工作生活平衡

3.8

薪酬

4.2

企业文化

4.3

职业发展

3.9

管理层

2.8

78%

推荐给朋友

优点

Flexible working hours and remote options

Great team culture and collaborative environment

Innovative projects and creative freedom

缺点

High workload and performance pressure

Fast-paced environment can be overwhelming

Management issues and disorganization

薪资范围

17个数据点

Junior/L3

Mid/L4

Senior/L5

Staff/L6

Junior/L3 · Associate Data Scientist

0份报告

$142,458

年薪总额

基本工资

-

股票

-

奖金

-

$121,089

$163,827

面试经验

3次面试

难度

3.0

/ 5

时长

14-28周

面试流程

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

常见问题

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit