Graduate Machine Learning Engineer – Risk & Graph ML

ByteDance; Singapore; On-site; SGD 180,000 - 280,000; Full time

Job summary

ByteDance is seeking a research-focused engineer in Singapore to advance risk detection and ecosystem governance on its e-commerce platform. You will leverage graph learning, multimodal data, and scalable ML models to identify risks in merchants, products, and creators, while supporting content understanding and governance workflows.

The role emphasizes collaboration, cutting-edge research, and practical deployment in a fast-growing tech environment.

Qualifications

• PhD in software development, CS, CE, or related field.
• Strong coding skills with DL frameworks: PyTorch, TensorFlow, DGL, pyg, sklearn.
• Knowledge of graph learning/sequence learning algorithms with depth and breadth.
• Industry ML experience with solid theoretical foundation.

Responsibilities

• Mine, identify, and predict risks and low-quality merchants, products, and creators using algorithms.
• Identify events affecting product experience and build data models for ecosystem health.
• Build data for entities like live streaming, products, merchants, and creators; develop large-scale graph models.
• Develop a graph storage and learning platform to improve relationship building in the community.
• Explore cutting-edge ML/graph/sequence learning technologies and apply them to scalable, production-ready models.

Skills

PyTorch

TensorFlow

Graph learning

Machine learning algorithms

Python

Education

PhD in Software Development / Computer Science / Computer Engineering or related field

Tools

Hive

Spark

Hadoop

Job description

ByteDance is seeking a research-focused engineer in Singapore to advance risk detection and ecosystem governance on its e-commerce platform. You will leverage graph learning, multimodal data, and scalable ML models to identify risks in merchants, products, and creators, while supporting content understanding and governance workflows.

The role emphasizes collaboration, cutting-edge research, and practical deployment in a fast-growing tech environment.