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채용

채용TikTok

Machine Learning Engineer Graduate (TikTok Shop Global E-Commerce, Risk Control) - 2026 Start (PhD)

TikTok

Machine Learning Engineer Graduate (TikTok Shop Global E-Commerce, Risk Control) - 2026 Start (PhD)

TikTok

Singapore

·

On-site

·

Full-time

·

2mo ago

복지 및 혜택

Unlimited Pto

Healthcare

Parental Leave

필수 스킬

Python

TensorFlow

PyTorch

Responsibilities

Team Introduction

The E-Commerce Risk Control (ECRC) team is missioned:

  • To protect Tiktok E-Commerce users, including and beyond buyer, seller, creator;
  • By securing the integrity of our ecommerce ecosystem and providing a safe shopping experience on the platform;
  • Through building infrastructures, platforms and technologies, as well as collaborating with many cross-functional teams and stakeholders.

In this team you'll have a unique and exciting opportunity to have first-hand exposure to build scalable and robust, intelligent and privacy-safe, secure and product-friendly systems via AI technology and data science products. Our challenges are not some regular day-to-day technical puzzles -- You'll be part of a team that's developing novel solutions to first-seen challenges of a non-stop evolvement of a phenomenal product eco-system.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at Tik Tok.

Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to Tik Tok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Key Responsibilities

  • Develop machine learning solutions for identifying and preventing various fraudulent activities.
  • Analyze massive business and security data to mine abnormal user behavior, and uncover evolving risky patterns.
  • Build data pipeline to enable scalable and real-time risk prevention.
  • Analyze / test the effectiveness of the built solutions.
  • Work in a cross-functional team setting to mitigate business risks.
  • Work with relevant software platform to develop/deploy/monitor the models.

Qualifications

Minimum Qualifications

  • Final year or recent PhD graduate with a background in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
  • Good coding skills in one or more programming language.

Preferred Qualifications

  • Familiar with machine learning frameworks such as scikit-learn, tensorflow, pytorch.
  • Ability to think critically, rationally, and communicate in result-oriented, data-driven manner.

Additional Information

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

If you have any questions, please reach out to us at apac-earlycareers@tiktok.com

연락처 및 위치

총 조회수

2

총 지원 클릭 수

0

모의 지원자 수

0

스크랩

0

TikTok 소개

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

직원 수

Los Angeles

본사 위치

$220B

기업 가치

리뷰

3.8

10개 리뷰

워라밸

2.8

보상

3.7

문화

4.1

커리어

3.2

경영진

2.9

68%

친구에게 추천

장점

Great team dynamics and support

Innovative and creative culture

Good learning opportunities

단점

Work-life balance challenges

Fast-paced and stressful environment

High expectations and tight deadlines

연봉 정보

49개 데이터

Mid/L4

Senior/L5

Mid/L4 · Applied AI Product Data Scientist

1개 리포트

$273,000

총 연봉

기본급

$210,000

주식

-

보너스

-

$273,000

$273,000

면접 경험

2개 면접

난이도

4.0

/ 5

소요 기간

21-35주

경험

긍정 0%

보통 0%

부정 100%

면접 과정

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Behavioral Interview

5

Final Round

6

Offer

자주 나오는 질문

Coding/Algorithm

Behavioral/STAR

Technical Knowledge

Culture Fit