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

About ByteDance

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.

Requirements

• 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.

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 1 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

• 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.

Topic Content

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

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

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.