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Machine Learning Engineer Graduate (App Ads and Gaming) - 2026 Start (BS/MS)

TikTok

Machine Learning Engineer Graduate (App Ads and Gaming) - 2026 Start (BS/MS)

TikTok

San Jose, CA

·

On-site

·

Full-time

·

1mo ago

Compensation

$118,657 - $177,000

Benefits & Perks

Wellness benefits

Annual team offsites

Learning and development stipend

Top Tier compensation with equity

Health, dental, and vision coverage

Required Skills

PyTorch

TensorFlow

Apache Spark

Responsibilities

The App Ads and Gaming team empowers Tik Tok's global monetization (billion-dollar business) via efficiently delivering application ads on Tik Tok. Our mission is to push the boundaries of large-scale ad delivery systems and lead the innovations of Tik Tok's personalized online advertising.

We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with 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 resumes.

Applications will be reviewed on a rolling basis. We encourage you to apply early.

Key Responsibilities

  • Build highly scalable machine learning systems and state-of-the-art machine learning models to improve ads ranking quality and optimize advertisers' marketing strategies. Examples include but are not limited to click through rate prediction, conversion rate prediction, intelligent format selection and user journey optimization.
  • Explore, develop and experiment with new features to improve model accuracy.
  • Understand ads platform objectives and take full advantage of modern machine learning to improve ads relevance, quality, and quantity delivered to end-users.
  • Collaborate with Product Managers, Designers, and other disciplines to explore the next generation of shopping experiences on Tik Tok.

Qualifications

Minimum Qualifications

  • BS/MS degree in Computer Science, Computer Engineering, or other relevant majors, with related work experience.
  • Solid programming skills, including but not limited to: Go, C/C++, Python. Familiar with basic data structure and algorithms. Familiar with Linux development environment.
  • Good analytical thinking capability. Have essential knowledge and skills in statistics.
  • Good theoretical grounding in the machine and deep learning concepts and techniques (CNN/RNN/LSTM, etc.).
  • Familiar with the architecture and implementation of at least one mainstream machine learning programming framework (Tensor Flow/Py Torch/MXNet), familiar with its architecture and implementation mechanism.

Preferred Qualifications

  • Good understanding in one of the following domains: ads bidding & auction, ads quality control, and online advertising systems (familiar with one or more of these terms: CPC/CPM, CTR/CVR, Ranking /Targeting, Conversion/Budget, Campaign/Creative, Demand/Inventory, DSP/RTB).
  • Experience in resource management and task scheduling with large-scale distributed software (such as Spark and Tensor Flow).
  • Relevant work or research experiences in search and recommendation.

Compensation

The base salary range for this position in the selected city is $118657 - $177000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

Los Angeles County (Unincorporated) Applicants

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

  1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
  2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
  3. Exercising sound judgment.

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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