Research Scientist - TikTok E-Commerce Recommendation Foundation

TikTok Inc.; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid parental leave

Disability coverage

Paid time off

Job summary

TikTok Inc. is advancing the next generation of recommendation intelligence through a dedicated E-commerce Recommendation Foundation team.

The role focuses on building unified Foundation Models for cross-business scenarios, spanning retrieval to re-ranking, with a strong emphasis on multimodal understanding and generative capabilities. Ideal candidates hold an advanced degree with a solid ML/IR background and a passion for research-driven engineering, including experience with large-scale model

Qualifications

• MS/PhD in Computer Science or related field or equivalent industrial research experience.
• Solid foundation in machine learning, deep learning, or information retrieval.
• Proficiency in Python and familiarity with PyTorch.
• Strong passion for intelligent recommendation systems and a self-driven research mindset.

Responsibilities

• Build and optimize cross-scenario shared Foundation Models to enable unified modeling and efficient inference.
• Advance event-sequence-driven generative recommendation with multimodal understanding and generative capabilities.
• Apply LLM technologies across retrieval, ranking, and re-ranking stages; contribute to training, inference optimization, and system co-design.
• Explore integration of LLMs/VLMs with recommendation systems to create adaptive, evolving intelligent recommenders.
• Research end-to-end generative recommendation and system optimization methods balancing efficiency and user experience.

Skills

Machine learning

Deep learning

Information retrieval

Python

PyTorch

Research mindset

Education

MS/PhD in Computer Science or related field

Tools

PyTorch

Job description

Responsibilities

About the Team

The E-commerce Recommendation Foundation team is dedicated to building the next-generation recommendation intelligence. We aim to develop a unified Foundation Model that supports multi-business and multi-scenario recommendation systems, covering the full pipeline from retrieval and ranking to re-ranking, and driving a comprehensive upgrade in intelligence and generative capability.

We believe the future of recommendation systems goes beyond predicting click-through rates - it lies in understanding the relationship between people and content, and in generating new connections. The team is exploring an event-sequence-driven generative recommendation paradigm, deeply integrating large language models (LLMs), multimodal understanding, reinforcement learning, and system optimization to advance recommendation systems toward general-purpose intelligent agents.

We value original exploration and encourage both research thinking and engineering excellence. Every team member is empowered to propose hypotheses and validate ideas in an open environment - your code and papers may help define the next paradigm of recommendation systems. We seek individuals with a general intelligence mindset to join us in redefining the future of recommendation.

1. Build and optimize cross-scenario shared Foundation Models to enable unified modeling and efficient inference.
2. Advance the event-sequence-driven generative recommendation paradigm, integrating multimodal understanding and generative capabilities.
3. Apply LLM technologies across retrieval, ranking, and re-ranking stages; participate in model training, inference optimization, and system co-design.
4. Explore the integration of LLMs / VLMs with recommendation systems to develop adaptive and evolving intelligent recommenders.
5. Research end-to-end generative recommendation and system optimization methods that balance efficiency and user experience.

Qualifications

Minimum Qualifications:

1. MS/PhD in Computer Science, related technical field or equivalent industrial research experience.
2. Solid theoretical foundation in machine learning, deep learning, or information retrieval.
3. Proficiency in Python and familiarity with mainstream deep learning frameworks (e.g., PyTorch).
4. Strong passion for intelligent recommendation systems and a self-driven research mindset.

Preferred Qualifications:

1. Experience in large-scale recommendation system development or large-model training, with notable technical achievements in a sub-area.
2. Research experience or publications in LLMs, multimodal learning, reinforcement learning, or generative recommendation.
3. Familiarity with pre-training and post-training processes for large language models (LLMs) or Foundation Models.

Approval

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $162000 - $387600 annually.

Compensation may vary outside of this range depending on 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 judgement.