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Staff Machine Learning Engineer

Spotify

Staff Machine Learning Engineer

Spotify

London

·

On-site

·

Full-time

·

2w 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.

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.

총 조회수

0

총 지원 클릭 수

0

모의 지원자 수

0

스크랩

0

Spotify 소개

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

직원 수

Stockholm

본사 위치

$23B

기업 가치

리뷰

4.1

10개 리뷰

워라밸

3.8

보상

4.2

문화

4.3

커리어

3.9

경영진

2.8

78%

친구에게 추천

장점

Flexible working hours and remote options

Great team culture and collaborative environment

Innovative projects and creative freedom

단점

High workload and performance pressure

Fast-paced environment can be overwhelming

Management issues and disorganization

연봉 정보

17개 데이터

Junior/L3

Mid/L4

Senior/L5

Staff/L6

Junior/L3 · Associate Data Scientist

0개 리포트

$142,458

총 연봉

기본급

-

주식

-

보너스

-

$121,089

$163,827

면접 경험

3개 면접

난이도

3.0

/ 5

소요 기간

14-28주

면접 과정

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

자주 나오는 질문

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit