Graduate AI Data Lakehouse Engineer (Realtime Storage)

TikTok; Singapore; On-site; SGD 70,000 - 120,000; Full time

Job summary

TikTok Singapore seeks graduates to join the Data Ecosystem Team, crafting storage for offline data in the billion-user recommendation system. You will design real-time and offline architectures, build scalable streaming Lakehouse systems, and collaborate with ML platforms to support PyTorch training and data pipelines.

Qualifications include degrees in AI/CS/CE, experience with distributed systems and Flink internals, and familiarity with Lakehouse tools like Iceberg/Delta Lake.

Qualifications

• Candidates completing or recently completed a Bachelor's or Master's in AI, Software Development, CS, or CE.
• Experience in 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 Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, incl. incremental ingestion, schema evolution, and snapshot isolation.

Responsibilities

• Design and implement real-time and offline data architectures for large-scale recommendation systems.
• Build scalable, high-performance 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 efficient data formats.
• Own core components of the distributed storage and processing stack, including file formats, stream compaction, and metadata management.

Skills

Distributed Systems

Flink internals

Lakehouse technologies

PyTorch

Data architectures

Java/Scala/C++

Education

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

Tools

Parquet

ORC

Lance

Delta Lake

Iceberg

Hudi

Job description

TikTok Singapore seeks graduates to join the Data Ecosystem Team, crafting storage for offline data in the billion-user recommendation system. You will design real-time and offline architectures, build scalable streaming Lakehouse systems, and collaborate with ML platforms to support PyTorch training and data pipelines.

Qualifications include degrees in AI/CS/CE, experience with distributed systems and Flink internals, and familiarity with Lakehouse tools like Iceberg/Delta Lake.