Data Engineer Graduate(TikTok Recommendation Ecosystem Architecture, Singapore) - 2027 Start (PhD)

ByteDance; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

ByteDance is seeking a Data Engineer Graduate (PhD) in Singapore to join the TikTok Recommendation Ecosystem Architecture team. The role focuses on building a unified infrastructure for training data and inference states used across search, recommendation, and advertising.

You will design real-time and offline data architectures, build scalable Lakehouse systems, and collaborate with ML platforms to optimize PyTorch workflows and large-scale data formats.

Qualifications

• PhD in Artificial Intelligence, Software Development, Computer Science or related field.
• Experience building large-scale distributed systems (storage, streaming, ML infra).
• Understanding of Apache Flink internals; hands-on with state management, connectors, or UDFs.
• Familiarity with Lakehouse technologies (Paimon, Iceberg, Delta Lake, or Hudi) especially incremental ingestion, schema evolution, snapshot isolation.

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

Skills

Distributed systems

Apache Flink internals

State management/UDFs

Lakehouse technologies

PyTorch integration

Java/Scala/C++

Data formats

Performance tuning

Education

PhD in AI/CS/related field

Tools

Apache Flink

Apache Paimon

Iceberg

Delta Lake

Hudi

Parquet/ORC/Lance

Job description

Data Engineer Graduate(TikTok Recommendation Ecosystem Architecture, Singapore) - 2027 Start (PhD)

Location:

Team:

Employment Type:

Regular

Job Code:

A223209

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Responsibilities

ResponsibilitiesBuilding a unified infrastructure that integrates the \"training data base\" and \"training/inference state system\" for multimodal foundation models in search, recommendation, and advertising scenarios. Through collaborative optimization of data lakes, caching, distributed computing, and GPU IO, we aim to reduce training and inference costs for foundation models while improving iteration efficiency.

• - 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

• - IIndividuals who are completing or have recently completed a PhD 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.
• - 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.
• - Knowledge of legacy data stores like HBase/Kudu.

Job Information

About Us

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.

Why Join ByteDance

Inspiring creativity is at the core of ByteDance's mission. Our innovative products are 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 enrich life - a mission we work towards every day.

As ByteDancers, 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. 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

ByteDance 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 ByteDance, our mission is to inspire creativity and enrich life. 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.