招聘
Mission Statement
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.
About the Team
Safe-and-Sound is the centralized Safety team within the AI Foundations Studio in Personalization. We build machine learning systems that help ensure Spotify experiences and recommendations are safe, responsible, and enjoyable across core surfaces like Home, Search, as well as newer generative AI experiences.
We partner closely with Tech Research, Trust & Safety, and Content Platform to develop new approaches in areas like synthetic data, fairness, and responsible AI. Our focus is on building scalable, high-impact systems that support both today’s products and the next generation of AI-driven experiences.
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关于Spotify

Spotify
PublicSpotify 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
4.5
职业发展
4.0
管理层
2.8
75%
推荐给朋友
优点
Flexible working hours and remote options
Great team culture and supportive colleagues
Creative freedom and innovative projects
缺点
High workload and stress during peak times
Fast-paced environment can be overwhelming
Management disorganization and direction issues
薪资范围
18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
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