Machine Learning System Engineer - Data AML - Soaring Star Talent Program

ByteDance; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

ByteDance is looking for research-oriented engineers to work on cutting-edge multimodal recommendation systems. Candidates should hold a doctoral degree in Computer Science or related fields and have significant programming experience in a Linux environment.

The role focuses on developing high-performance frameworks and conducting research, offering a vibrant work environment and growth opportunities in Singapore's dynamic tech scene.

Qualifications

• Holds a doctoral degree, preferably in Computer Science or Software Engineering.
• Proficiency in programming languages (C/C++/Go/Python/Java) in a Linux environment.
• Deep understanding of and experience with large-scale distributed systems.

Responsibilities

• Conduct research on multimodal recommendation systems.
• Develop high-performance multimodal inference and training frameworks.
• Collaborate in the design of architectures for recommendation-ads and multimodal co-training.

Skills

C/C++/Go/Python/Java

Distributed system principles

Technical documentation

Education

Doctoral degree in Computer Science or related fields

Tools

TensorFlow

PyTorch

Kubernetes

Job description

Responsibilities

Team Introduction: Data AML is ByteDance's machine learning middle platform, providing training and inference systems for recommendation, advertising, CV (computer vision), speech, and NLP (natural language processing) across businesses such as Douyin, Toutiao, and Xigua Video. AML provides powerful machine learning computing capabilities to internal business units and conducts research on general and innovative algorithms to solve key business challenges. Additionally, through Volcano Engine, it delivers core machine learning and recommendation system capabilities to external enterprise clients. Beyond business applications, AML is also engaged in cutting‑edge research in areas such as AI for Science and scientific computing.

Research Project Introduction

Large‑scale recommendation systems are being increasingly applied to short video, text community, image and other products, and the role of modal information in recommendation systems has become more prominent. ByteDance's practice has found that modal information can serve as a generalization feature to support business scenarios such as recommendation, and the research on end‑to‑end ultra‑large‑scale multimodal recommendation systems has enormous potential. It is expected to further explore directions such as multimodal co‑training, 7B/13B large‑scale parameter models, and longer sequence end‑to‑end based on algorithm‑engineering CoDesign.

Engineering Research Directions

• Representation of multimodal samples
• Construction of high‑performance multimodal inference engines based on the PyTorch framework
• Development of high‑performance multimodal training frameworks
• Application of heterogeneous hardware in multimodal recommendation systems

Algorithmic Research Directions

• Design of reasonable recommendation‑advertising and multimodal co‑training architectures
• Sparse Mixture of Experts (Sparse MOE)
• Memory Network
• Hybrid precision techniques

Qualifications

Minimum Qualifications

• Holds a doctoral degree, with preference for candidates majoring in Computer Science, Software Engineering, or related fields.
• Proficiency in one or more programming languages such as C/C++/Go/Python/Java in a Linux environment.
• Deep understanding of distributed system principles, with experience in designing, developing, and maintaining large‑scale distributed systems.
• Possess excellent logical analysis skills, able to abstract and decompose complex business logic effectively, with a collaborative team spirit.
• Strong sense of responsibility, with good learning ability, communication skills, and self‑motivation.
• Good habits in technical documentation, including timely writing and updating of work processes and technical docs as required.

Preferred Qualifications

• Familiarity with Kubernetes architecture and extensive experience in cloud‑native system development.
• Experience with at least one mainstream machine learning framework (e.g., TensorFlow, PyTorch, MXNet).
• Familiarity with Django, Flask, or related technologies, with backend development experience.
• Experience in one or more of the following areas:
  • AI Infrastructure
  • HW/SW Co-Design
  • High‑Performance Computing
  • ML Hardware Architecture (GPU, accelerators, networking)
  • Machine Learning Frameworks
  • ML for Systems
  • Distributed Storage
  • Experience in large‑scale cloud computing platforms or private cloud product architecture development.