Machine Learning Engineer, Ads Targeting

Responsibilities

The Ads Targeting's goal is to help advertisers reach their desired audience and optimize advertisement performance. As a member of the Ads Targeting, Ads Core team, you will apply machine learning models to scale budgets by understanding user interest and intention, and build large-scale foundations for data processing and serving for next-generation ad targeting products. This team is working on a variety of products such as custom audience, interest, behavior, lookalike, auto targeting etc., as well as new innovative features.

We are seeking Machine Learning Engineers who can help us to improve our existing delivery system that optimizes for advertisers' true business objectives, i.e. desired user value and effectiveness of ROI. You will have a chance to work with a fully globalized team made up of great engineering talents in different countries, and work closely with cross-functional teams to build proper and relevant connections between users, advertisers, and TikTok.

What You'll Do:
• Responsible for the development of state-of-the-art applied machine learning projects.
• Own key targeting components or strategies in the TikTok ads monetization ecosystem.
• Build scalable platforms and pipelines for ads targeting products.
• Work with product and business teams on the product vision.

Qualifications

Minimum Qualifications:
• BS/MS degree in Computer Science, Statistics, Operation Research, Applied Mathematics, Physics or similar quantitative fields, with related experience.
• Hands-on experience in one or more of the following areas: machine learning, deep learning, statistical models and applied mathematical methods.
• Strong coding skills, especially in Python/C++/Go. Experience with high-load systems.
• Familiarity with online experimentation and analytics.
• Familiarity with big data systems including Hadoop and Spark.
• Curiosity towards new technologies and entrepreneurship.

Preferred Qualifications:
• Experience in reinforcement learning, transfer learning, and counter-factual optimization is a plus.
• Understanding of the business value of online advertising.
• Experience with user modeling using deep learning methods from large scale datasets. Experience with privacy preserving modeling techniques is a plus.
• Experience in developing modern ads ranking/retrieval/targeting systems and recommender systems.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $162000 - $316800 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 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.

For 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.

Responsibilities

Responsibilities

The Ads Targeting's goal is to help advertisers reach their desired audience and optimize advertisement performance. As a member of the Ads Targeting, Ads Core team, you will apply machine learning models to scale budgets by understanding user interest and intention, and build large-scale foundations for data processing and serving for next-generation ad targeting products. This team is working on a variety of products such as custom audience, interest, behavior, lookalike, auto targeting etc., as well as new innovative features.

We are seeking Machine Learning Engineers who can help us to improve our existing delivery system that optimizes for advertisers' true business objectives, i.e. desired user value and effectiveness of ROI. You will have a chance to work with a fully globalized team made up of great engineering talents in different countries, and work closely with cross-functional teams to build proper and relevant connections between users, advertisers, and TikTok.

What You'll Do:
• Responsible for the development of state-of-the-art applied machine learning projects.
• Own key targeting components or strategies in the TikTok ads monetization ecosystem.
• Build scalable platforms and pipelines for ads targeting products.
• Work with product and business teams on the product vision.

Qualifications

Qualifications

Minimum Qualifications:
• BS/MS degree in Computer Science, Statistics, Operation Research, Applied Mathematics, Physics or similar quantitative fields, with related experience.
• Hands-on experience in one or more of the following areas: machine learning, deep learning, statistical models and applied mathematical methods.
• Strong coding skills, especially in Python/C++/Go. Experience with high-load systems.
• Familiarity with online experimentation and analytics.
• Familiarity with big data systems including Hadoop and Spark.
• Curiosity towards new technologies and entrepreneurship.

Preferred Qualifications:
• Experience in reinforcement learning, transfer learning, and counter-factual optimization is a plus.
• Understanding of the business value of online advertising.
• Experience with user modeling using deep learning methods from large scale datasets. Experience with privacy preserving modeling techniques is a plus.
• Experience in developing modern ads ranking/retrieval/targeting systems and recommender systems.