Research Engineer Intern (AI Infrastructure) - Large Model Applications - Global Frontier Tech Recruitment Program - 2027 Start (PhD)
Apr 16, 2026 Singapore
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 in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.
PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).
Team Introduction: Our Team is responsible for the design and development of the Recommendation and Search system architecture for TikTok. It ensures the stability and high availability of the system, optimizes the performance of online services and offline data streams, resolves system bottlenecks, and reduces cost overheads. The team also abstracts the common components and services of the system, builds the recommendation middle - office and data middle - office to support the rapid incubation of new products and enable ToB services.
Topic Content: As business scenarios become increasingly complex, search, advertising, and recommendation are facing significant challenges. While large models can accurately capture user preferences and enhance personalization as well as content quality, they also impose stringent requirements on real-time performance, stability, and scalability. This introduces substantial technical challenges in areas such as distributed training, inference acceleration, heterogeneous hardware utilization, and multimodal data processing.
At the same time, the rapid growth in model scale and the proliferation of multimodal data have made it difficult for existing infrastructure to meet the demands of data processing efficiency and resource utilization. This topic focuses on system and engineering innovations to overcome key technical bottlenecks and build efficient, stable, and scalable large-model solutions, providing a robust technical foundation for search, advertising, and recommendation scenarios.
Topic Challenges:
1. Native training and inference architecture redesign for LLMs
2. Etreme performance optimization and AI infrastructure innovation
3. End-to-End generative paradigm innovation
4. Multimodal AU data infrastructure and quality pptimization
5. Multimodal data representation and RAG-based application system
Topic Value: Building next-generation generative AI infrastructure for search, advertising, and recommendation businesses. Through the co-design of large models, multimodal technologies, and system-level innovations, we aim to overcome performance bottlenecks and enable ultra-long context handling, millisecond-level response latency, and high-precision information understanding, thereby driving intelligent upgrades across the business.
Qualifications
Minimum Qualifications
1. Currently pursuing PhD in Artificial Intelligence, Computer Science, Computer Engineering, or a related technical discipline.
2. Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required. Candidates are required to use these languages to implement complex algorithms and build iterative models. Candidates should also have a strong engineering mindset with the ability to balance performance and cost;
Preferred Qualifications
1. Priority will be given to candidates with in-depth research results in journals or conferences such as VLDB, SIGIR, OSDI, etc.
2. Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.
We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.
PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).
Team Introduction: Our Team is responsible for the design and development of the Recommendation and Search system architecture for TikTok. It ensures the stability and high availability of the system, optimizes the performance of online services and offline data streams, resolves system bottlenecks, and reduces cost overheads. The team also abstracts the common components and services of the system, builds the recommendation middle - office and data middle - office to support the rapid incubation of new products and enable ToB services.
Topic Content: As business scenarios become increasingly complex, search, advertising, and recommendation are facing significant challenges. While large models can accurately capture user preferences and enhance personalization as well as content quality, they also impose stringent requirements on real-time performance, stability, and scalability. This introduces substantial technical challenges in areas such as distributed training, inference acceleration, heterogeneous hardware utilization, and multimodal data processing.
At the same time, the rapid growth in model scale and the proliferation of multimodal data have made it difficult for existing infrastructure to meet the demands of data processing efficiency and resource utilization. This topic focuses on system and engineering innovations to overcome key technical bottlenecks and build efficient, stable, and scalable large-model solutions, providing a robust technical foundation for search, advertising, and recommendation scenarios.
Topic Challenges:
1. Native training and inference architecture redesign for LLMs
2. Etreme performance optimization and AI infrastructure innovation
3. End-to-End generative paradigm innovation
4. Multimodal AU data infrastructure and quality pptimization
5. Multimodal data representation and RAG-based application system
Topic Value: Building next-generation generative AI infrastructure for search, advertising, and recommendation businesses. Through the co-design of large models, multimodal technologies, and system-level innovations, we aim to overcome performance bottlenecks and enable ultra-long context handling, millisecond-level response latency, and high-precision information understanding, thereby driving intelligent upgrades across the business.
Minimum Qualifications
1. Currently pursuing PhD in Artificial Intelligence, Computer Science, Computer Engineering, or a related technical discipline.
2. Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required. Candidates are required to use these languages to implement complex algorithms and build iterative models. Candidates should also have a strong engineering mindset with the ability to balance performance and cost;
Preferred Qualifications
1. Priority will be given to candidates with in-depth research results in journals or conferences such as VLDB, SIGIR, OSDI, etc.
2. Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.
We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.
PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).
Team Introduction: Our Team is responsible for the design and development of the Recommendation and Search system architecture for TikTok. It ensures the stability and high availability of the system, optimizes the performance of online services and offline data streams, resolves system bottlenecks, and reduces cost overheads. The team also abstracts the common components and services of the system, builds the recommendation middle - office and data middle - office to support the rapid incubation of new products and enable ToB services.
Topic Content: As business scenarios become increasingly complex, search, advertising, and recommendation are facing significant challenges. While large models can accurately capture user preferences and enhance personalization as well as content quality, they also impose stringent requirements on real-time performance, stability, and scalability. This introduces substantial technical challenges in areas such as distributed training, inference acceleration, heterogeneous hardware utilization, and multimodal data processing.
At the same time, the rapid growth in model scale and the proliferation of multimodal data have made it difficult for existing infrastructure to meet the demands of data processing efficiency and resource utilization. This topic focuses on system and engineering innovations to overcome key technical bottlenecks and build efficient, stable, and scalable large-model solutions, providing a robust technical foundation for search, advertising, and recommendation scenarios.
Topic Challenges:
1. Native training and inference architecture redesign for LLMs
2. Etreme performance optimization and AI infrastructure innovation
3. End-to-End generative paradigm innovation
4. Multimodal AU data infrastructure and quality pptimization
5. Multimodal data representation and RAG-based application system
Topic Value: Building next-generation generative AI infrastructure for search, advertising, and recommendation businesses. Through the co-design of large models, multimodal technologies, and system-level innovations, we aim to overcome performance bottlenecks and enable ultra-long context handling, millisecond-level response latency, and high-precision information understanding, thereby driving intelligent upgrades across the business.
Qualifications
Minimum Qualifications
1. Currently pursuing PhD in Artificial Intelligence, Computer Science, Computer Engineering, or a related technical discipline.
2. Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required. Candidates are required to use these languages to implement complex algorithms and build iterative models. Candidates should also have a strong engineering mindset with the ability to balance performance and cost;
Preferred Qualifications
1. Priority will be given to candidates with in-depth research results in journals or conferences such as VLDB, SIGIR, OSDI, etc.
2. Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.
Responsibilities
ResponsibilitiesWe are looking for talented individuals to join us for an internship in 2027. PhD Internships at our Company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.
PhD internships at Our Company provides students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.
Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).
Team Introduction: Our Team is responsible for the design and development of the Recommendation and Search system architecture for TikTok. It ensures the stability and high availability of the system, optimizes the performance of online services and offline data streams, resolves system bottlenecks, and reduces cost overheads. The team also abstracts the common components and services of the system, builds the recommendation middle - office and data middle - office to support the rapid incubation of new products and enable ToB services.
Topic Content: As business scenarios become increasingly complex, search, advertising, and recommendation are facing significant challenges. While large models can accurately capture user preferences and enhance personalization as well as content quality, they also impose stringent requirements on real-time performance, stability, and scalability. This introduces substantial technical challenges in areas such as distributed training, inference acceleration, heterogeneous hardware utilization, and multimodal data processing.
At the same time, the rapid growth in model scale and the proliferation of multimodal data have made it difficult for existing infrastructure to meet the demands of data processing efficiency and resource utilization. This topic focuses on system and engineering innovations to overcome key technical bottlenecks and build efficient, stable, and scalable large-model solutions, providing a robust technical foundation for search, advertising, and recommendation scenarios.
Topic Challenges:
1. Native training and inference architecture redesign for LLMs
2. Etreme performance optimization and AI infrastructure innovation
3. End-to-End generative paradigm innovation
4. Multimodal AU data infrastructure and quality pptimization
5. Multimodal data representation and RAG-based application system
Topic Value: Building next-generation generative AI infrastructure for search, advertising, and recommendation businesses. Through the co-design of large models, multimodal technologies, and system-level innovations, we aim to overcome performance bottlenecks and enable ultra-long context handling, millisecond-level response latency, and high-precision information understanding, thereby driving intelligent upgrades across the business.
Qualifications
QualificationsMinimum Qualifications
1. Currently pursuing PhD in Artificial Intelligence, Computer Science, Computer Engineering, or a related technical discipline.
2. Excellent programming abilities with a strong command of data structures and fundamental algorithms. For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required. Candidates are required to use these languages to implement complex algorithms and build iterative models. Candidates should also have a strong engineering mindset with the ability to balance performance and cost;
Preferred Qualifications
1. Priority will be given to candidates with in-depth research results in journals or conferences such as VLDB, SIGIR, OSDI, etc.
2. Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.
Additional Information
- Posted: Apr 16, 2026