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Machine Learning Engineer - Feed E-Commerce - Singapore

TikTok

Machine Learning Engineer - Feed E-Commerce - Singapore

TikTok

Singapore

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Remote work flexibility

Top Tier compensation with equity

Wellness benefits

Parental leave program

Health, dental, and vision coverage

Required Skills

Python

TensorFlow

PyTorch

Responsibilities

Team Intro

We Are the Tik Tok ROW E-commerce Short Video&Live Recommendation Team. As pioneers reshaping global shopping experiences, we specialize in end-to-end optimization of short video recommendation systems across Europe, Southeast Asia, and Latin America. Our mission spans the full recommendation pipeline - from content supply, candidate retrieval, pre-ranking, ranking, blending to user experience refinement - building a culture-adaptive recommendation engine for Tik Tok's diverse markets.

Breaking through traditional "product shelf" e-commerce paradigms, we reinvent recommendation systems for the short video era. Our team combines academic excellence from top global universities with industrial expertise in billion-DAU recommendation systems. By leveraging cutting-edge machine learning technologies, we create dynamic intelligent matching bridges between massive product catalogs and global users.

We are committed to providing a personalized, proactive, and efficient consumption experience for users through live-stream e-commerce content by connecting them with exceptional sellers and high-quality products.

Our team is responsible for developing innovative recommendation algorithms and techniques to enhance user engagement and satisfaction, effectively transforming creative ideas into business-impacting solutions.

Responsibilities

  • Design and apply machine learning algorithm and recommendation strategies to improve users' experience on e-commerce content, including videos and livestreams.
  • Understand ecosystem of e-commerce content and use algorithm and strategy to make it thrive.
  • Work with product and ops team to deliver features that drives growth of e-commerce content on Tik Tok.
  • Build industry leading recommendation system; develop highly scalable classifiers and tools leveraging machine learning

Qualifications

Minimum Qualifications

  1. Bachelor's degree in computer science or a related technical discipline, with at least 2 years of related work experience
  2. Solid experience with data structures and algorithms
  3. Software development experience through hands on coding in a general purpose programming language
  4. Experience in one or more of the following areas: machine learning, recommendation systems, data mining or other related areas
  5. Strong communication and teamwork skills
  6. Passion about technologies and solving challenging problems

Preferred Qualifications

  • Preferred to have more than 3 years experience in the recommendation algorithm domain

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About TikTok

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

Employees

Los Angeles

Headquarters

$220B

Valuation

Reviews

3.1

3 reviews

Work Life Balance

1.5

Compensation

2.0

Culture

1.2

Career

1.8

Management

1.0

5%

Recommend to a Friend

Pros

Limited positive feedback available

Company size allows for potential opportunities

Technology platform experience

Cons

Mass layoffs and poor handling of terminations

Unprofessional management and HR behavior

Exposure to traumatic content without adequate support

Salary Ranges

52 data points

Mid/L4

Senior/L5

Mid/L4 · Applied AI Product Data Scientist

1 reports

$273,000

total / year

Base

$210,000

Stock

-

Bonus

-

$273,000

$273,000

Interview Experience

4 interviews

Difficulty

3.5

/ 5

Duration

21-35 weeks

Experience

Positive 0%

Neutral 25%

Negative 75%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Interviews

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Data Structures