Research Scientist Intern - LLM Applications for International E-commerce Scenarios - Global Tech Research Program - 2027 Start (PhD)

Topic Content:
In today's global e-commerce landscape, intelligent systems must operate across increasingly complex and dynamic business environments. Yet existing approaches still face limitations in long-horizon forecasting, cross-modal understanding, and holistic decision-making.This initiative is focused on building a next-generation foundational large model purpose-built for global e-commerce applications. The model will integrate key business dimensions-such as users, products, content, logistics, and inventory-into a unified representation to support deep, context-aware intelligence at scale.

Topic Challenges:
1. Heterogeneous fusion and alignment: Unified modeling of user behavior sequences, product sales time-series signals, and multimodal product content to achieve deep semantic alignment across high-dimensional temporal data and multimodal representations.
2. Synergy between recommendation LLMs and world models: Reformulating the recommendation problem as a generative task of producing ranked item lists for users, and leveraging large model technologies to enable end-to-end recommendation modeling.
3. Tokenizer of recommendation items: Designing scalable tokenization mechanisms to encode billions of items into multimodal and semantically rich representations, supporting training and generation tasks. This includes pretraining over tens of terabytes of user behavior tokens, improving scaling law performance through optimized model architectures and training strategies, and reframing diverse recommendation tasks as post-training objectives. Recommendation modeling is further enhanced using RLVR-style approaches to maximize GMV and user experience. In addition, training and inference are co-optimized, with high-performance recommendation systems built on large-model inference frameworks such as SGLang.
4. Multimodal large models for e-commerce: Developing multilingual, multimodal large models tailored for e-commerce, achieving state-of-the-art (SOTA) performance across core e-commerce scenarios. Building on this foundation, we establish an e-commerce agent backbone to enable scalable deployment of agent applications across diverse use cases.
5. Agent evaluation, safety, and compliance: Establishing evaluation metrics and benchmarks aligned with real-world business scenarios to ensure the robustness, safety, and compliance of agent systems, particularly under highly constrained and adversarial conditions.

Topic Value:
1. Building a general-purpose multimodal foundation to enable power-law scaling through iterative advancements in models, data, and compute, thereby strengthening the infrastructure for scalable AI foundations.
2. Establishing a global e-commerce foundation model to drive GMV growth and user retention through generative recommendation, time-series large models, and agent-based systems.

Qualifications

Minimum Qualifications
1. Currently pursuing PhD in Computer Science, AI, Mathematics, or a related technical discipline, with a strong foundation in data structures, algorithms, and mathematical modeling.
2. AI/ML Expertise: Solid understanding and research experience in Deep Learning, NLP, CV, Reinforcement Learning, Generative Models, or Multimodal Learning.

Preferred Qualifications
1. Priority will be given to candidates with publications in international AI/CS conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, KDD, SIGIR, WWW) or top rankings in recognized algorithmic competitions.
2. Excellent programming abilities in leading or participating in key projects related to Search, Advertising, Recommendation systems, or Large Language Models (LLMs).
3. Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.

Client-provided location(s): Singapore

Job ID: TikTok-7629211025884154165

Employment Type: INTERN

Posted: 2026-04-16T20:21:34