Global Frontier Tech Recruitment Program - 2027 Grad Singapore Regular

ByteDance; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

ByteDance is seeking a Research Scientist for their Applied Machine Learning team to develop next-generation machine learning technologies. This role focuses on challenges in large-scale recommendation systems, requiring a PhD in a related field and expertise in distributed systems and programming.

The successful candidate will tackle significant problems such as multimodal representation fusion, enhancing recommendation accuracy, and cloud-native system development. Opportunities for growth and innovative challenges await you at ByteDance.

Qualifications

• Individuals who are completing or recently completed a PhD in a related technical discipline.
• Proficiency in programming languages such as C/C++/Go/Python/Java in a Linux environment.
• Deep understanding of distributed system principles.

Responsibilities

• Join a team committed to advancing machine learning core technologies.
• Work on challenges in unifying multimodal representations.
• Contribute to research on recommendation systems.

Skills

C/C++/Go/Python/Java

Distributed systems

Cloud-native system development

Machine learning frameworks

Education

PhD in Artificial Intelligence, Computer Science, or related discipline

Tools

Kubernetes

TensorFlow

PyTorch

Job description

Research Scientist - Large-Scale Machine Learning Systems (SysML) - Global Frontier Tech Recruitment Program - 2027 Start (PhD)

Responsibilities

Team Introduction: The Applied Machine Learning (AML) team is committed to the research and deployment of the next‑generation of machine learning core technologies. This covers large pre‑trained models and device‑cloud collaboration learning, as well as wide applications in search, recommendation, advertising, auditing, federated learning, and more. The team has a strong foundation in scientific research, engineering and product implementation. Our team members have rich backgrounds covering natural language processing (NLP), computer vision (CV), multimodality, graph computing, search and recommendation, federated learning and other fields, and have published more than 100 top‑tier conference papers.

We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company. Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.

Topic Content: Large‑scale recommendation systems are being increasingly adopted across products such as short‑video, text‑based multimodal, and image platforms, with modality‑specific in tremendous laying an ever‑growing role in recommendations. Leveraging our latest research breakthroughs and broad industry insights, we believe modality information serves effectively as generalizable features to support recommendation and other business scenarios. Research on ultra‑large‑scale multimodal recommendation systems holds significant potential.

Topic Challenges:

• High difficulty in unifying multimodal representations and achieving efficient fusion.
• Extremely high computational costs for training and inference of ultra‑large‑parameter models.
• Difficulty balancing stability and efficiency in end‑to‑end modeling with long sequences.
• Significant complexity in algorithm‑engineering co‑design and heterogeneous hardware adaptation.

Topic Value:

• Technical value: Achieve breakthroughs in multimodal representation fusion and training/inference bottlenecks for ultra‑large‑scale models; refine the co‑design framework for algorithms and engineering; advance heterogeneous hardware adaptation and the development and deployment of domestically developed high‑performance frameworks.
• Business value: Enhance recommendation accuracy and generalization capability in multimodal scenarios; overcome the modality limitations of existing recommendation systems; empower multiple products including short‑video and text‑based community platforms; reduce computational costs; and drive scalable business growth.

Qualifications

Minimum Qualifications:

• Individuals who are completing or recently completed a PhD in Artificial Intelligence, Computer Science, Computer Engineering, or a related technical discipline.
• 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.

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.

EEO Statement

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.