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

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

Benefits offered by this job

Stock options

Yoga and fitness

Paid leave

Flat organization

Career growth opportunity

Job summary

TikTok is seeking talented graduates to join our Arch-Data Ecosystem team, building offline and real-time data storage for large-scale recommendation, search, and ads at a billion-user scale. You will design data architectures, collaborate on storage, and optimize data pipelines for efficiency and cost.

Successful candidates will contribute to a unified training data base and state system, with opportunities to tackle bold ideas and advance in a rapid-growth tech environment.

Qualifications

• Candidates completing or recently completed a PhD in AI, CS, CE or software-related field.
• Experience building large-scale distributed systems, preferably storage, streaming, or ML infra.
• Familiarity with Flink internals, state management, connectors or UDFs.
• Knowledge of Lakehouse tech: Paimon, Iceberg, Delta Lake, or Hudi, incl. schema evolution and snapshots.

Responsibilities

• Build unified infra for training data base and training/inference state for multimodal models in search, recommendation, and ads.
• Collaborate on data lakes, caching, distributed computing, and GPU IO to reduce costs and improve iteration efficiency.
• Own components of distributed storage and processing stack from file formats to metadata management.
• We seek graduates to pursue bold ideas and grow within the team.

Education

PhD in Artificial Intelligence

PhD in Computer Science

PhD in Computer Engineering

PhD in Software Development

Tools

Apache Flink

Apache Paimon

Iceberg

Delta Lake

Hudi

PyTorch

Job description

About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is 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 bring joy - a mission we work towards every day.

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. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. 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

TikTok 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 TikTok, our mission is to inspire creativity and bring joy. 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.