AI Feature/Computing/Storage Engineer Graduate (TikTok Recommendation Ecosystem Architecture, Singapore) - 2027 Start

BYTEDANCE PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Yoga & fitness

Stock options

Team atmosphere

Career growth

Paid leave

Flat organization

100M+ users

Industry experts

Competitive pay

Flexible hours

Job summary

ByteDance is seeking a data engineering professional to design real-time and offline data architectures for its trillion-scale recommendation system in the TikTok ecosystem. You will build scalable Lakehouse systems powering feature pipelines, model training, and real-time inference, and collaborate with ML platform teams to support PyTorch workflows.

Successful candidates will work on distributed storage, stream processing, and metadata management to enable efficient data access across

Qualifications

• Bachelor's degree in AI, Computer Science, or related field.
• Experience building large-scale distributed systems in storage, stream processing, or ML infra.
• Strong knowledge of Apache Flink state management, connectors, or UDFs.
• Familiarity with Lakehouse technologies: Paimon, Iceberg, Delta Lake or Hudi.

Responsibilities

• 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 workflows and data formats.
• Own components of distributed storage and processing stack, including metadata management.

Skills

Distributed systems

Flink internals

Lakehouse tech

Java/Scala/C++

Parquet/ORC

PyTorch pipelines

Education

Bachelor's degree in AI/CS/CE

Tools

Apache Flink

Iceberg

Delta Lake

Hudi

Paimon

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.

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.

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:

The TikTok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in TikTok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions.

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.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities

• 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:

• Individuals who are completing or have recently completed a Bachelor's degree or above 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.
• Solid 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 is a plus.
• (Optional) Knowledge of legacy data stores like HBase/Kudu is a bonus but not required.