Research Scientist Intern - Large-Scale Machine Learning …, Singapore

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.

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, covering large pretrained models, device‑cloud collaborative learning, and wide applications in search, recommendation, advertising, auditing, federated learning, and more. Our team has a strong foundation in scientific research, engineering and product implementation, with rich backgrounds covering natural language processing, computer vision, multimodality, graph computing, search and recommendation, federated learning and other fields.

Topic Content: 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.

We aim to further explore directions including multimodal co‑training, ultra‑large‑scale models, end‑to‑end modeling with extended sequence lengths, multimodal sample representation, high‑performance inference engines built on PyTorch, training framework development, and algorithm‑engineering co‑design including heterogeneous hardware adaptation.

• 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.

Technical and business value: We aim to achieve breakthroughs in multimodal representation fusion, training and inference bottlenecks for ultra‑large‑scale models, refine co‑design frameworks, advance heterogeneous hardware adaptation, enhance recommendation accuracy and generalization, empower multiple products, reduce computational costs, and drive scalable business growth.

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, or 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.
• In‑depth research results and extensive practical experience in AI Infrastructure, HW/SW Co‑Design, High‑Performance Computing, ML Hardware Architecture, ML for Systems, distributed storage.