Applied Scientist - Monetization Technology - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

Responsibilities

We are looking for talented individuals to join our team in 2027. 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 our Company.
Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.

Team Introduction: Global Monetization Product and Technology team are building the next-generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok. Our team develops a wide variety of advertisements for numerous uses including feeds, live streaming, branding, measurement, targeting, search, vertical solutions, creative solutions, and business integrity.

Topic Content: This topic dives deep into TikTok's core global advertising scenarios, driving innovation and implementation of the cutting-edge generative technologies in search, recommendation, and advertising. By deeply integrating foundation models with the advertising business, we address key technical challenges in Large Recommender Models and Large Language Models (LLMs) to build a next-generation intelligent advertising engine with autonomous decision-making capabilities.

Our research covers cutting-edge directions, including Large Recommender Model scaling laws, end-to-end unified modeling, generative full-link technologies (retrieval, ranking, AIGC material generation, bidding), intelligent advertising placement agents, ultra-long sequence modeling, and causal inference. We tackle extreme challenges of trillion-level features and millisecond responses, advancing advertising recommendation toward the foundation model paradigm to achieve dual improvements in monetization efficiency and user experience.

Responsibilities:
1. Explore scaling laws for foundation models in recommendation and advertising, and build a foundation model based on unified multimodal semantic modeling.
2. Build an intelligent ad placement system optimized for users' Long-Term Value (LTV) and long-term ROAS, achieving an optimal balance between commercial value and user experience.
3. Optimize the full-process training and online inference framework for foundation models, balance computing power costs and real-time response performance, and resolve the performance-latency trade-off in real-world deployment.

Qualifications

Minimum Qualifications:
1. Individuals who are completing or have recently completed a PhD in Computer Science, Computer Engineering, or a related technical discipline.
2. Modeling experience in one or more of the areas: Ads, Search engine, Recommender System, NLP/CV.
3. Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and
practical experience in areas such as single-modal LLM application and deployment.

Preferred Qualifications:
1. Priority will be given to candidates with research results and extensive practical relevant fields, such as outstanding performance in natural language processing, computer vision, data modeling, or algorithm optimization, etc.
2. Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required.
3. Strong publications record in top conferences (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, NACCL, CVPR, ICCV, and ECCV) is a plus.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $218400 - $480000 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

We are looking for talented individuals to join our team in 2027. 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 our Company.
Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.

Team Introduction: Global Monetization Product and Technology team are building the next-generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok. Our team develops a wide variety of advertisements for numerous uses including feeds, live streaming, branding, measurement, targeting, search, vertical solutions, creative solutions, and business integrity.

Topic Content: This topic dives deep into TikTok's core global advertising scenarios, driving innovation and implementation of the cutting-edge generative technologies in search, recommendation, and advertising. By deeply integrating foundation models with the advertising business, we address key technical challenges in Large Recommender Models and Large Language Models (LLMs) to build a next-generation intelligent advertising engine with autonomous decision-making capabilities.

Our research covers cutting-edge directions, including Large Recommender Model scaling laws, end-to-end unified modeling, generative full-link technologies (retrieval, ranking, AIGC material generation, bidding), intelligent advertising placement agents, ultra-long sequence modeling, and causal inference. We tackle extreme challenges of trillion-level features and millisecond responses, advancing advertising recommendation toward the foundation model paradigm to achieve dual improvements in monetization efficiency and user experience.

Responsibilities:
1. Explore scaling laws for foundation models in recommendation and advertising, and build a foundation model based on unified multimodal semantic modeling.
2. Build an intelligent ad placement system optimized for users' Long-Term Value (LTV) and long-term ROAS, achieving an optimal balance between commercial value and user experience.
3. Optimize the full-process training and online inference framework for foundation models, balance computing power costs and real-time response performance, and resolve the performance-latency trade-off in real-world deployment.

Qualifications

Qualifications

Minimum Qualifications:
1. Individuals who are completing or have recently completed a PhD in Computer Science, Computer Engineering, or a related technical discipline.
2. Modeling experience in one or more of the areas: Ads, Search engine, Recommender System, NLP/CV.
3. Have a solid foundation in algorithms related to LLMs, including but not limited to comprehensive learning and
practical experience in areas such as single-modal LLM application and deployment.

Preferred Qualifications:
1. Priority will be given to candidates with research results and extensive practical relevant fields, such as outstanding performance in natural language processing, computer vision, data modeling, or algorithm optimization, etc.
2. Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required.
3. Strong publications record in top conferences (e.g., ICLR, NeurIPS, ICML, ACL, EMNLP, NACCL, CVPR, ICCV, and ECCV) is a plus.