Data Engineer - Real-Time Lakehouse & ML Infra

BYTEDANCE PTE. LTD.; Singapore; On-site; SGD 180,000 - 260,000; Full time

Benefits offered by this job

Yoga & Fitness

Stock options

Career growth

Paid leave

Flat organization

100M+ users

Industry experts

Competitive compensation

Flexible hours

Job summary

ByteDance seeks highly skilled engineers to join the Arch-Data Ecosystem team, contributing to data storage and processing for billions of users. You will tackle offline and real-time data challenges, build cost-efficient storage and fast data pipelines, and collaborate with ML platforms to streamline model training workflows.

Ideal candidates hold a PhD in AI/CS or related fields, have experience in distributed systems, and are proficient with modern lakehouse technologies and large-scale data

Qualifications

• PhD in AI/CS or related; emphasis on distributed systems/ML infra.
• Experience with large-scale distributed systems in storage, streaming, or ML infra.
• Familiarity with Flink internals, state management, connectors, or UDFs.

Responsibilities

• Build a unified infra integrating training data base and training/inference state system for multimodal foundation models across search, recommendation, and advertising.
• Design real-time and offline data architecture for large-scale recommendation systems.
• Build scalable Lakehouse systems powering feature pipelines, model training, and real-time inference.
• Collaborate with ML platform teams to support PyTorch-based training workflows and efficient data formats for large samples.

Skills

Distributed systems design

Data lakehouse concepts

Java/Scala/C++

Performance tuning

Big data tooling

Education

PhD in Artificial Intelligence, Computer Science, or related field

Tools

Apache Flink

Apache Paimon

Iceberg

Delta Lake

Hudi

HBase

Kudu

Job description

ByteDance seeks highly skilled engineers to join the Arch-Data Ecosystem team, contributing to data storage and processing for billions of users. You will tackle offline and real-time data challenges, build cost-efficient storage and fast data pipelines, and collaborate with ML platforms to streamline model training workflows.

Ideal candidates hold a PhD in AI/CS or related fields, have experience in distributed systems, and are proficient with modern lakehouse technologies and large-scale data