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 ads delivery systems and lead the innovations of Tik Tok's personalized online advertising. Hence, you'll have a chance to get deeply involved in our large-scale distributed systems and state-of-the-art machine-learning algorithms. As a Machine Learning Engineer on the App Ads & Gaming team, you will make efforts to develop novel machine learning solutions for ranking, build scalable foundations, and launch various products that maximize the efficiency of deep funnel app ads delivery.

We are looking for talented individuals to join our team in 2025. 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 2025. We will prioritize candidates who are able to commit to these start dates. Please state your availability and graduation date clearly in your resume. Applications will be reviewed on a rolling basis. We encourage you to apply early.

Candidates can apply for a maximum of TWO positions and will be considered for jobs in the order you applied for. The application limit is applicable to Tik Tok and its affiliates' jobs globally.

Key Responsibilities:

• Work on developing a large-scale App Ads delivery system to improve accuracy.
• Explore multi-stage ranking models, conduct data analysis, and develop feature engineering.
• Participate in the innovations of core App Ads systems using machine learning models, ranking algorithms, etc.
• Partner with product managers, data scientists, and product strategy & operation team to launch new App Ads, products, and features.

Qualifications

Minimum Qualifications:

• BS/MS degree in Computer Science, Computer Engineering, or other relevant majors.
• Excellent programming, debugging, and optimization skills in one or more general-purpose programming languages, including but not limited to Go, C/C++, and Python.
• Familiarity with online experimentation methods and analytics. Essential knowledge and skills in statistics.
• Curiosity toward new technologies and entrepreneurship.

Preferred Qualification:

• Experience with recommender system projects. Experience with applying machine learning methods for ranking.

Compensation

The base salary range for this position in the selected city is $118,657 - $259,200 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) Candidates

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.