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

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

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.

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.

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:

Our Arch-Data Ecosystem team plays a crucial role in the data ecosystem of the TikTok Recommendation System, focusing on creating offline and real-time data storage solutions for large-scale recommendation, search, and advertising businesses, serving over 1 billion users. The core goals of the team are to ensure high system reliability, uninterrupted service, and smooth data processing. We are committed to building a storage and computing infrastructure that can adapt to various data sources and meet diverse storage requirements, ultimately providing efficient, cost-effective, and user-friendly data storage and management tools for the business.

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

Building 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.