Global Frontier Tech Recruitment Program - Intern Singapore

ByteDance; Singapore; On-site; SGD 25,000 - 45,000; Full time

Job summary

ByteDance is offering a Research Scientist Intern position in Singapore for 2027, focusing on large-scale machine learning systems. This internship will allow PhD candidates to contribute to groundbreaking research while collaborating with industry experts.

The ideal intern should be passionate about AI, have a solid understanding of programming languages and experience with machine learning frameworks. Join ByteDance and be part of innovative projects that shape the future of technology.

Qualifications

• Currently pursuing 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.

Responsibilities

• Contribute to research and deployment of next-generation machine learning technologies.
• Explore multimodal recommendation systems.
• Participate in algorithm-engineering co-design.

Skills

Proficiency in C/C++/Go/Python/Java

Familiarity with Kubernetes

Experience with machine learning frameworks

Backend development with Django/Flask

Education

Currently pursuing PhD in AI, CS, or related field

Job description

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

Employment Details

Employment Type: Intern

Job Code: A258328

Responsibilities

We are looking for talented individuals to join us for an internship in 2027. PhD Internships at our company aim to provide students with the opportunity to actively contribute to our products and research, and to the organization’s future plans and emerging technologies. Our dynamic internship experience blends hands‑on learning, community building, and development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis – we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date). 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 pretrained 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, with backgrounds covering NLP, CV, multimodality, graph computing, search and recommendation, federated learning and other fields, and has published more than 100 top‑tier conference papers. Large‑scale recommendation systems are increasingly adopted across products such as short‑video, text‑based community, and image platforms, with modality‑specific information playing an ever‑growing role in recommendations. In our practice, modality information serves effectively as generalizable features to support recommendation and other business scenarios. Research on end‑to‑end ultra‑large‑scale multimodal recommendation systems holds significant potential. Building on an algorithm‑engineering co‑design approach, we aim to further explore directions including multimodal co‑training, models with hundreds of billions of parameters, and end‑to‑end modeling with extended sequence lengths. On the engineering side, research directions include multimodal sample representation; high‑performance multimodal inference engines built on the PyTorch framework; high‑performance multimodal training framework development; and the application of heterogeneous hardware in multimodal recommendation systems. On the algorithm side, research directions include designing effective recommendation‑and‑ads multimodal co‑training architectures, sparse MoE, memory networks, and mixed precision.

Topic Challenges:

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

Topic Value:

1. 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.
2. 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

• Currently pursuing 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.

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.
• Priority will be given to candidates with in‑depth research results and extensive practical experience in relevant fields: AI Infrastructure, HW/SW Co‑Design, High‑Performance Computing, ML Hardware Architecture (GPU, accelerators, networking), Machine Learning Frameworks, ML for Systems, Distributed Storage.