Research Scientist Intern - TikTok Recommendation(NextGen LLM) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

s (recommendation metrics), and also producing state-of-the-art research outputs.

Project Overview, Challenges & Value
We aim to integrate recommendation large models multimodal large models, and the Agentic Rec framework to fundamentally reshape the underlying content distribution logic, empowering the system with deep semantic association and autonomous planning capabilities, exploring new frontiers in algorithmic design.

1. Recommendation Large Models: Address challenges such as gradient convergence and representation drift in ultra-long behavioral sequences, enabling the system to achieve true ""logical reasoning"" capabilities.
2. Unified Multimodal Semantic Space: Explore alignment across video, image-text content, and user intent, constructing a fully multimodal semantic space that goes beyond text.
3. Agentic Rec:Develop recommendation agents with capabilities such as self-reflection, tool invocation, and long-horizon planning, driving a transformation of recommendation and content distribution experiences.

Key challenges include:

1. Extreme-scale reasoning:
   Performance gains and bottlenecks associated with ultra-large model parameters and ultra-long sequence modeling.
2. Multimodal fusion: Challenges in representation learning for cross-modal intent alignment.
3. Autonomous evolution: Breakthroughs in long-horizon planning and decision-making paradigms for agent systems.

Project Value:

1. Technical value: Explore new paradigms for recommendation, significantly improving recommendation performance and system efficiency.
2. Business value: Enable deeper understanding of user interests and content, improving distribution efficiency and enhancing satisfaction for both users and creators.

Responsibilities:

• Design and develop next-generation large-scale recommendation systems optimized for personalized, engaging, and scalable user experiences.
• Leverage state-of-the-art machine learning and deep learning techniques, including large model technologies (LLM and MLLM, etc), to enhance recommendation performance and accuracy.
• Collaborate with cross-disciplinary teams, including infrastructure engineers, pmo, and researchers, to create advanced systems that improve recommendation relevance, diversity, and user engagement.

Qualifications

Minimum Qualifications:

1. Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
2. Experience in one of more areas of computer vision, natural language processing and machine learning
3. Solid knowledge and experience with at least one major deep learning framework (e.g. PyTorch, Tensorflow, MXNet, Caffe/Caffe2).
4. Familiar with deep neural network architectures such as transformer/SSM/CNN/RNN/LSTM etc.
   Strong analytical and problem solving skills. Ability to work collaboratively in cross-functional teams.

Preferred Qualifications:

• Research experience demonstrated through projects, publications, or open-source contributions.
• Authors with publications in top-tier venues such as SIGGRAPH, SIGGRAPH Asia, CVPR, ICCV, ECCV, ICML, NeurIPS, ICLR,