AI Feature/Computing/Storage Engineer Graduate (TikTok Recommendation Ecosystem Architecture) - 2027 Start

TIKTOK PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Benefits offered by this job

Stock options

Competitive compensation

Flexible hours

Paid leave

Job summary

TikTok PTE. LTD. is seeking graduates with strong foundations in AI/CS to help design and implement high-performance data architectures for a billion-user recommendation system.

You will work on real-time and offline data pipelines, streaming Lakehouse systems, and collaboration with ML platforms to optimize model training data workflows. The role includes opportunities to contribute to scalable storage solutions and data management tools.

Qualifications

• Bachelor's or Master's in AI, CS, or CE.
• Experience building large-scale distributed systems.
• Familiar with Flink internals and state management.
• Knowledge of Lakehouse technologies (Paimon, Iceberg, Delta Lake, Hudi).
• Preferred: Flink+Paimon architectures, PyTorch data pipelines, Parquet/ORC formats, Java/Scala/C++ proficiency, lakehouse metadata management.

Responsibilities

• Design and implement real-time and offline data architecture for large-scale recommendation systems.
• Build scalable streaming Lakehouse systems powering feature pipelines, model training, and real-time inference.
• Collaborate with ML platform teams to support PyTorch-based model training workflows and data formats for large-scale samples and features.
• Own core components of the distributed storage and processing stack, from file format to stream compaction and metadata management.

Skills

Distributed systems

Flink internals

Java/Scala/C++

Model training data pipelines

Education

Bachelor's or Master's in AI/CS/CE

Tools

Apache Flink

Apache Paimon

Iceberg

Delta Lake

Hudi

PyTorch

Job description

About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.

We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an \"Always Day 1\" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

Job highlights

• Yoga and fitness


• Stock options


• Positive team atmosphere


• Career growth opportunity


• Paid leave


• Flat organization


• 100+ mil users


• Industry experts


• Competitive compensation


• Flexible hours

Responsibilities

Team Introduction:

The TikTok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in TikTok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.

Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities

• Design and implement real-time and offline data architecture for large-scale recommendation systems.


• Build scalable and high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.


• Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats and access patterns for large-scale samples and features.


• Own core components of our distributed storage and processing stack, from file format to stream compaction to metadata management.

Qualifications

Minimum Qualifications:

• Individuals who are completing or have recently completed a Bachelor's or Master's degree in Artificial Intelligence, Software Development, Computer Science, Computer Engineering or a related discipline.


• Experience building large-scale distributed systems, preferably in storage, stream processing, or ML infrastructure.


• Solid understanding of Apache Flink internals, with hands-on experience in state management, connectors, or UDFs.


• Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, especially around incremental ingestion, schema evolution, and snapshot isolation.

Preferred Qualifications:

• Experience in designing and optimizing Flink + Paimon architectures for unified batch/stream processing.


• Familiarity with feature storage and training data pipelines, and their integration with PyTorch, especially for large-scale model training.


• Knowledge of columnar file formats (Parquet, ORC, Lance) and how they are used in feature engineering or ML data loading.


• Proficiency in Java/Scala/C++, and strong debugging/performance tuning ability.


• Previous experience in Lakehouse metadata management, compaction scheduling, or data versioning is a plus.


• (Optional) Knowledge of legacy data stores like HBase/Kudu is a bonus but not required.