Research Scientist - E-commerce Recommendation(LLM Applications) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

2. Collaboration Between Recommendation LLMs and World Models:
Reformulating recommendation as a generative problem of producing user-specific recommendation lists, enabling end-to-end modeling based on large models.
3. Item Tokenization for Recommendation:
Efficiently encoding hundreds of millions of items into multimodal semantic representations to support large-scale training and generation tasks. Handling tens of terabytes of user behavior tokens during pretraining, improving scaling laws through model architecture and training strategies, reframing recommendation tasks into post-training problems (e.g., RLVR-based approaches), and optimizing for GMV and user experience. Building high-performance recommendation systems using inference frameworks such as SGLang.
4. Multimodal Large Models for E-commerce:
Developing multilingual and multimodal large models tailored for e-commerce, achieving state-of-the-art (SOTA) performance in core scenarios, and serving as the foundation for intelligent e-commerce agents across diverse applications.
5. Agent Evaluation, Safety & Compliance:
Designing evaluation metrics and benchmarks aligned with real-world business scenarios, ensuring robustness, safety, and compliance under highly constrained and adversarial environments.

Project Value
- Technical Value - Build a general-purpose multimodal foundation model, leveraging iterative improvements in models, data, and compute to achieve scaling-law-driven growth and establish a strong technical foundation.
- Business Value - Establish a foundational large model for Global E-commerce, leveraging generative recommendation, temporal models, and agent-based systems to drive GMV growth and user retention, forming a high-leverage revenue engine.

Responsibilities
- Design algorithms and systems that leverage LLMs and generative models for content-to-commerce matching, product summarization, etc
- Explore novel architectures and strategies for generative recommendation systems
- Contribute to the research community via internal papers, patents, or external publications
- Drive scientific rigor while balancing real-world constraints

Qualifications

Minimum Qualifications
1. Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
2. Strong foundation in machine learning, with knowledge of cutting-edge AI technologies; publications in accredited academic conferences or competition experience are preferred.
3. Familiarity with big data frameworks such as Hadoop, MapReduce, and Spark.
4. Experience with TensorFlow or PyTorch for model training and deployment; understanding of training acceleration techniques such as mixed precision and distributed training.

Preferred Qualifications
1. Knowledge of model compression and inference acceleration techniques, including but not limited to quantization, pruning, distillation, and TensorRT optimization.
2. Expertise in at least one of the following areas:
- Computer Vision & Multimodality: In-depth research experience in multimedia or computer vision fields, including but not limited to image search, image/video classification and recognition, image segmentation, object detection, OCR, graph neural networks, multimodal learning, and unsupervised/self-supervised learning. Experience with large-scale CV/multimodal models, particularly in e-commerce scenarios, including developing and optimizing multimodal models for e-commerce videos and products. Ability to integrate LLMs with video/product representations to support tasks such as multimodal classification, video QA, cross-modal retrieval, and product categorization, with performance significantly surpassing production models. Strong hands-on experience, with achievements in competitions such as Kaggle, COCO, ImageNet, ActivityNet, or ICPC. Familiarity with state-of-the-art research, with publications in accredited conferences such as CVPR, ICCV, or ECCV.
- Natural Language Processing (NLP): In-depth research experience in NLP, including but not limited to pretraining techniques, natural language understanding, multilingual and cross-lingual learning, natural language generation, transfer learning, and semi-supervised learning. Experience with large language models (LLMs), including developing NLP models to unify tasks in e-commerce scenarios and applying them in real-world business contexts. Strong practical experience, with achievements in competitions such as Kaggle, GLUE, SuperGLUE, or CLUE. Familiarity with state-of-the-art research, with publications in accredited conferences such as ACL or EMNLP.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $202160 - $427500 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.

Client-provided location(s): Seattle, WA

Job ID: TikTok-7629243930022267189

Employment Type: OTHER

Posted: 2026-04-17T20:43:46