Research Scientist - TikTok E-Commerce Recommendation Foundation

TikTok; Seattle (WA); On-site; USD 154,000 - 301,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with match

Parental leave

Disability insurance

Wellbeing benefits

Paid holidays

Job summary

TikTok is expanding its E-commerce Recommendation Foundation team to build next-generation foundation models for cross-scenario use, enabling unified modeling and efficient inference. The role emphasizes LLM/VM integration, retrieval and ranking optimization, and advancing generative recommendation with scalable systems.

You will contribute to research and engineering efforts with a focus on practical impact and innovation in a fast-moving tech environment.

Qualifications

• MS or PhD in CS or related field, or equivalent industrial research experience.
• Strong ML/DL theory and information retrieval fundamentals.
• Proficiency in Python and PyTorch.
• Passion for intelligent recommendation systems and a self-driven research mindset.

Responsibilities

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

Skills

MS/PhD in Computer Science

ML/DL theory

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.

Responsibilities

• 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.

About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day. We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

TikTok Accommodation

TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request

Job Information

【For Pay Transparency】Compensation Description (Annually) The base salary range for this position in the selected city is $153900 - $300960 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;
• 3. Exercising sound judgment.