Tech Lead Research Scientist - Recommendation Foundation Models (Global E-commerce)

Responsibilities

About the Team
TikTok's Recommendation Foundation team builds shared foundation models for Global E-commerce. We are advancing event-sequence-driven generative recommendation with LLMs/VLMs, multimodal understanding, and reinforcement learning-moving beyond click prediction toward recommendation agents that understand user intent and connect people with products and content.

We are looking for researchers and engineers with demonstrated LLM impact in industry or academia, or experience building and deploying generative recommendation systems at scale. You will shape foundation-model approaches for Global E-commerce and take them from research to production.

Responsibilities
- Own foundation models from research to production: define the modeling strategy and lead data, architecture, pre-training, mid-training, and post-training through deployment.
- Advance LLM-native, event-sequence-driven recommendation: model user behavior and intent to improve retrieval, ranking, and end-to-end generative recommendation.
- Build multimodal representations and semantic tokenizers that connect product, content, and behavioral signals and transfer across scenarios.
- Deliver measurable online impact: establish rigorous evaluations and experiments, balancing recommendation quality and long-term user value with latency and inference cost.
- Lead through hands-on execution: guide model and system design, align research and engineering partners, and mentor colleagues while staying close to code and experiments.

Qualifications

Minimum Qualifications:
- MS/PhD in Computer Science, related technical field or equivalent industrial research experience.
- Demonstrated impact in LLMs through industry systems or academic research, or hands-on experience building and deploying large-scale generative recommendation systems.
- Strong machine learning and deep learning fundamentals, with expertise in LLMs, foundation models, or generative recommendation
- Ability to lead complex technical work from problem definition through evaluation and deployment, and collaborate across research and engineering.

Preferred Qualifications:
- Experience setting research roadmaps, leading shared modeling platforms or cross-team initiatives, and mentoring researchers and engineers.
- Experience with foundation-model training or post-training, reinforcement learning, or preference optimization.
- Expertise in generative retrieval, semantic tokenization, multimodal understanding, long-sequence user modeling, or efficient training and inference.
- Evidence of impact through production launches, reusable technical contributions, or publications at leading ML/NLP venues.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $241680 - $456000 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