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Big Data Engineer Graduate (TikTok Live Recommendation Architecture) - 2026 Start (BS/MS)

TikTok

Big Data Engineer Graduate (TikTok Live Recommendation Architecture) - 2026 Start (BS/MS)

TikTok

Singapore

·

On-site

·

Full-time

·

2mo ago

福利厚生

Equity

Learning

Parental Leave

必須スキル

React

Python

PostgreSQL

Big Data Engineer Graduate (Tik Tok Live Recommendation Architecture) - 2026 Start (BS/MS)

3+ months ago• Singapore
Apply on company site

About Us

Tik Tok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
Size: 5001-10000 employees

Industry: Entertainment & Gaming, Social Media, Technology

Responsibilities:

About the Team:

Our Tik Tok Live Recommendation Architecture Team is responsible for building up and optimizing the architecture for live broadcast recommendation system to provide the most stable and best experience for our Tik Tok users. The team is responsible for system stability and high availability, online services and offline data flow performance optimization, solving system bottlenecks, reducing cost overhead, building data and service mid-platform, realizing flexible and scalable high-performance storage and computing systems. We work closely with applied machine learning engineers and build scalable systems to support all kinds of innovative algorithms and techniques.

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.

Responsibilities:

  • Design and implement reasonable offline data architecture for large-scale recommendation systems.
  • Design and implement flexible, scalable, and high-performance storage and computing systems.
  • Trouble-shooting of the production system, design and implement the necessary mechanisms and tools to ensure the stability of the overall operation of the production system.
  • Build industry-leading distributed systems such as storage and computing to provide reliable infrastructure for massive data and large-scale business systems.
  • Develop and implement techniques and analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualisation software.

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  • Apply data mining, data modeling, natural language processing, and machine learning to extract and analyse information from large structured and unstructured datasets.
  • Visualise, interpret, and report data findings and may create data reports as well.

Qualifications:

Minimum Qualifications:

  • Final year or recent graduate with a background in Software Development, Computer Science, Computer Engineering, or a related technical discipline.

  • Familiar with many open source frameworks in the field of big data, e.g.

  • Hadoop, Hive, Flink, FlinkSQL, Spark, Kafka, HBase, Redis, RocksDB, Elastic Search etc.

  • Familiar with Java, C ++ and other programming languages.

  • Strong coding and trouble shooting ability.

Preferred Qualifications:

  • Agile, quick self learner, highly self-motivated with strong sense of product ownership and creative problem solver.
  • Good collaborator and team player, comfortable working in a fast moving, culturally diverse and globally distributed team environment.

By submitting an application for this role, you accept and agree to our global applicant If you have any questions, please reach out to us at apac-earlycareers@tiktok.com

Client-provided location(s): Singapore

Job ID: Tik Tok-7531731901713074439

Employment Type: OTHER

Posted: 2025-07-29T01:28:02
Apply on company site

Perks and Benefits

Health and Wellness

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • HSA
  • Life Insurance
  • Fitness Subsidies
  • Short-Term Disability
  • Long-Term Disability
  • On-Site Gym
  • Mental Health Benefits
  • Virtual Fitness Classes

Parental Benefits

  • Fertility Benefits
  • Adoption Assistance Program
  • Family Support Resources

Work Flexibility

  • Flexible Work Hours
  • Hybrid Work Opportunities

Office Life and Perks

  • Casual Dress
  • Snacks
  • Pet-friendly Office
  • Happy Hours
  • Some Meals Provided
  • Company Outings
  • On-Site Cafeteria
  • Holiday Events

Vacation and Time Off

  • Paid Vacation
  • Paid Holidays
  • Personal/Sick Days
  • Leave of Absence

Financial and Retirement

  • 401(K) With Company Matching
  • Performance Bonus
  • Company Equity

Professional Development

  • Promote From Within
  • Access to Online Courses
  • Leadership Training Program
  • Associate or Rotational Training Program
  • Mentor Program

Diversity and Inclusion

  • Diversity, Equity, and Inclusion Program
  • Employee Resource Groups (ERG)

Company Videos

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総閲覧数

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模擬応募者数

0

スクラップ

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TikTokについて

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

従業員数

Los Angeles

本社所在地

$220B

企業価値

レビュー

3.8

10件のレビュー

ワークライフバランス

2.8

報酬

3.7

企業文化

4.1

キャリア

3.2

経営陣

2.9

68%

友人に勧める

良い点

Great team dynamics and support

Innovative and creative culture

Good learning opportunities

改善点

Work-life balance challenges

Fast-paced and stressful environment

High expectations and tight deadlines

給与レンジ

49件のデータ

Junior/L3

Junior/L3 · Anti-Fraud Data Analyst

3件のレポート

$143,750

年収総額

基本給

$125,000

ストック

-

ボーナス

-

$126,500

$163,300

面接体験

2件の面接

難易度

4.0

/ 5

期間

21-35週間

体験

ポジティブ 0%

普通 0%

ネガティブ 100%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Behavioral Interview

5

Final Round

6

Offer

よくある質問

Coding/Algorithm

Behavioral/STAR

Technical Knowledge

Culture Fit