AI Feature/Computing/Storage Engineer Intern (TikTok Recommendation Ecosystem) - 2027 Start (PhD)
Aug 3, 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: 5,001 - 10,000 employees
- Industry: Entertainment & Gaming
Responsibilities
Team Introduction
Our Arch-Data Ecosystem team plays a crucial role in the data ecosystem of the TikTok Recommendation System, focusing on creating offline and real-time data storage solutions for large-scale recommendation, search, and advertising businesses, serving over 1 billion users. The core goals of the team are to ensure high system reliability, uninterrupted service, and smooth data processing. We are committed to building a storage and computing infrastructure that can adapt to various data sources and meet diverse storage requirements, ultimately providing efficient, cost-effective, and user-friendly data storage and management tools for the business.
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).
Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities
- Conduct research on next-generation streaming lakehouse architectures and data processing systems for large-scale recommendation feature pipelines
- Design and implement optimized data formats and storage systems to support high-throughput deep learning model training workloads
- Develop novel data management and indexing techniques to reduce end-to-end latency of feature engineering pipelines for recommendation systems
Qualifications
Minimum Qualifications:
- Currently pursuing a PhD in Computer Science, engineering or quantitative field
- Strong programming skills in Java/Scala/C++, with research experience in distributed systems, databases, or data management
- Published research in data systems at venues including SIGMOD, VLDB, ICDE, SOSP, OSDI, NSDI, or equivalent conferences
Preferred Qualifications:
- Hands-on experience with big data frameworks (Apache Flink, Spark) or lakehouse technologies (Apache Paimon, Iceberg, Delta Lake)
- Research background in columnar storage formats, vector databases, or data indexing techniques
- Strong background in machine learning systems or large-scale data processing systems
Team Introduction
Our Arch-Data Ecosystem team plays a crucial role in the data ecosystem of the TikTok Recommendation System, focusing on creating offline and real-time data storage solutions for large-scale recommendation, search, and advertising businesses, serving over 1 billion users. The core goals of the team are to ensure high system reliability, uninterrupted service, and smooth data processing. We are committed to building a storage and computing infrastructure that can adapt to various data sources and meet diverse storage requirements, ultimately providing efficient, cost-effective, and user-friendly data storage and management tools for the business.
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).
Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities
- Conduct research on next-generation streaming lakehouse architectures and data processing systems for large-scale recommendation feature pipelines
- Design and implement optimized data formats and storage systems to support high-throughput deep learning model training workloads
- Develop novel data management and indexing techniques to reduce end-to-end latency of feature engineering pipelines for recommendation systems
Minimum Qualifications:
- Currently pursuing a PhD in Computer Science, engineering or quantitative field
- Strong programming skills in Java/Scala/C++, with research experience in distributed systems, databases, or data management
- Published research in data systems at venues including SIGMOD, VLDB, ICDE, SOSP, OSDI, NSDI, or equivalent conferences
Preferred Qualifications:
- Hands-on experience with big data frameworks (Apache Flink, Spark) or lakehouse technologies (Apache Paimon, Iceberg, Delta Lake)
- Research background in columnar storage formats, vector databases, or data indexing techniques
- Strong background in machine learning systems or large-scale data processing systems
Team Introduction
Our Arch-Data Ecosystem team plays a crucial role in the data ecosystem of the TikTok Recommendation System, focusing on creating offline and real-time data storage solutions for large-scale recommendation, search, and advertising businesses, serving over 1 billion users. The core goals of the team are to ensure high system reliability, uninterrupted service, and smooth data processing. We are committed to building a storage and computing infrastructure that can adapt to various data sources and meet diverse storage requirements, ultimately providing efficient, cost-effective, and user-friendly data storage and management tools for the business.
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).
Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities
- Conduct research on next-generation streaming lakehouse architectures and data processing systems for large-scale recommendation feature pipelines
- Design and implement optimized data formats and storage systems to support high-throughput deep learning model training workloads
- Develop novel data management and indexing techniques to reduce end-to-end latency of feature engineering pipelines for recommendation systems
Qualifications
Minimum Qualifications:
- Currently pursuing a PhD in Computer Science, engineering or quantitative field
- Strong programming skills in Java/Scala/C++, with research experience in distributed systems, databases, or data management
- Published research in data systems at venues including SIGMOD, VLDB, ICDE, SOSP, OSDI, NSDI, or equivalent conferences
Preferred Qualifications:
- Hands-on experience with big data frameworks (Apache Flink, Spark) or lakehouse technologies (Apache Paimon, Iceberg, Delta Lake)
- Research background in columnar storage formats, vector databases, or data indexing techniques
- Strong background in machine learning systems or large-scale data processing systems
Responsibilities
ResponsibilitiesTeam Introduction
Our Arch-Data Ecosystem team plays a crucial role in the data ecosystem of the TikTok Recommendation System, focusing on creating offline and real-time data storage solutions for large-scale recommendation, search, and advertising businesses, serving over 1 billion users. The core goals of the team are to ensure high system reliability, uninterrupted service, and smooth data processing. We are committed to building a storage and computing infrastructure that can adapt to various data sources and meet diverse storage requirements, ultimately providing efficient, cost-effective, and user-friendly data storage and management tools for the business.
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).
Successful candidates must be able to commit to at least 3 months long internship period.
Responsibilities
- Conduct research on next-generation streaming lakehouse architectures and data processing systems for large-scale recommendation feature pipelines
- Design and implement optimized data formats and storage systems to support high-throughput deep learning model training workloads
- Develop novel data management and indexing techniques to reduce end-to-end latency of feature engineering pipelines for recommendation systems
Qualifications
QualificationsMinimum Qualifications:
- Currently pursuing a PhD in Computer Science, engineering or quantitative field
- Strong programming skills in Java/Scala/C++, with research experience in distributed systems, databases, or data management
- Published research in data systems at venues including SIGMOD, VLDB, ICDE, SOSP, OSDI, NSDI, or equivalent conferences
Preferred Qualifications:
- Hands-on experience with big data frameworks (Apache Flink, Spark) or lakehouse technologies (Apache Paimon, Iceberg, Delta Lake)
- Research background in columnar storage formats, vector databases, or data indexing techniques
- Strong background in machine learning systems or large-scale data processing systems
Additional Information
- Posted: Aug 3, 2026