Research Scientist Intern - TikTok Recommendation(NextGen LLM) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)
Apr 29, 2026 San Jose
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 10,000+ employees
- Industry: Technology: Consumer Internet
Why you should apply for a job to TikTok:
4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
Responsibilities
We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.
Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
About the team
Our team's mission is to empower genAI and content understanding in TikTok businesses. Computer vision and natural language processing are important dimensions in both content generation and understanding. We are working on various foundational models, including multi-modality pretraining, multi-modal large language model, image generation, video generation etc. As a genAI team on the business side, we try to succeed in both achieving business metric gains (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:
- Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
- Experience in one of more areas of computer vision, natural language processing and machine learning
- Solid knowledge and experience with at least one major deep learning framework (e.g. PyTorch, Tensorflow, MXNet, Caffe/Caffe2).
- 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,
We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.
Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
About the team
Our team's mission is to empower genAI and content understanding in TikTok businesses. Computer vision and natural language processing are important dimensions in both content generation and understanding. We are working on various foundational models, including multi-modality pretraining, multi-modal large language model, image generation, video generation etc. As a genAI team on the business side, we try to succeed in both achieving business metric gains (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.
Minimum Qualifications:
- Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
- Experience in one of more areas of computer vision, natural language processing and machine learning
- Solid knowledge and experience with at least one major deep learning framework (e.g. PyTorch, Tensorflow, MXNet, Caffe/Caffe2).
- 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,
We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.
Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
About the team
Our team's mission is to empower genAI and content understanding in TikTok businesses. Computer vision and natural language processing are important dimensions in both content generation and understanding. We are working on various foundational models, including multi-modality pretraining, multi-modal large language model, image generation, video generation etc. As a genAI team on the business side, we try to succeed in both achieving business metric gains (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:
- Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
- Experience in one of more areas of computer vision, natural language processing and machine learning
- Solid knowledge and experience with at least one major deep learning framework (e.g. PyTorch, Tensorflow, MXNet, Caffe/Caffe2).
- 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,
Responsibilities
ResponsibilitiesWe are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies.
Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
About the team
Our team's mission is to empower genAI and content understanding in TikTok businesses. Computer vision and natural language processing are important dimensions in both content generation and understanding. We are working on various foundational models, including multi-modality pretraining, multi-modal large language model, image generation, video generation etc. As a genAI team on the business side, we try to succeed in both achieving business metric gains (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
QualificationsMinimum Qualifications:
- Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related field.
- Experience in one of more areas of computer vision, natural language processing and machine learning
- Solid knowledge and experience with at least one major deep learning framework (e.g. PyTorch, Tensorflow, MXNet, Caffe/Caffe2).
- 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,
Additional Information
- Posted: Apr 29, 2026