AI Data Platform Engineer: Lakehouse & ML Pipelines

TikTok; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

TikTok is seeking an AI Feature/Computing/Storage Engineer Graduate for the 2027 start, based in Singapore. You will help design and implement data architectures for large-scale recommendation systems, focusing on real-time and offline processing, and collaborating on PyTorch training workflows.

A strong foundation in distributed systems and Flink is essential. The role offers exposure to Lakehouse technologies and end-to-end data pipelines, with opportunities to scale across multiple data

Qualifications

• Bachelor's or Master's degree in AI, Software Development, Computer Science, Computer Engineering or related field.
• 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.

Responsibilities

• Design and implement real-time and offline data architecture for large-scale recommendation systems.
• Build scalable and 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 design efficient data formats.

Skills

Distributed systems

State management

Debugging

Performance tuning

Education

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

Tools

Apache Flink

Lakehouse tech (Paimon/Iceberg/Delta Lake/Hudi)

Parquet/ORC/Lance

Java/Scala/C++

Job description

TikTok is seeking an AI Feature/Computing/Storage Engineer Graduate for the 2027 start, based in Singapore. You will help design and implement data architectures for large-scale recommendation systems, focusing on real-time and offline processing, and collaborating on PyTorch training workflows.

A strong foundation in distributed systems and Flink is essential. The role offers exposure to Lakehouse technologies and end-to-end data pipelines, with opportunities to scale across multiple data