Research Scientist Intern - Risk Control Vertical LLM Foundation and Agent - Global Frontier Tech Recruitment Program

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.

PhD internships at Our Company provides 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, enriching 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).

Team Introduction: The Risk Control R&D Team is dedicated to addressing various challenges posed by malicious activities across products. Their work spans multiple domains of risk governance such as content, transactions, traffic, and accounts. By leveraging technologies such as machine learning, multimodal models, and large models, the team strives to understand user behaviors and content, thereby identifying potential risks and issues. By continuously deepening their understanding of business and user behaviors, the team drives innovation in models and algorithms with an aim to build an industry-leading risk control algorithm system.

Topic Content: Current leading large models struggle with understanding highly adversarial risky content and identifying AI-generated content (AIGC). At the same time, underground activities online change quickly and need fast responses. To meet this challenge, risk control needs to develop agent-based autonomous systems that can fight threats more effectively and reduce operational costs. Risk control also faces challenges due to the large amount of data and complex rules involved. It needs better ways to extract cross-modal long-context information and follow complex compliance instructions. This topic aims to improve risk control intelligence across all scenarios by optimizing large models from end to end, building smart agent systems, and creating new paradigms.

Topic Challenges:

• Insufficient understanding of underground industry variants, AIGC, and other adversarial content by general large models
• Challenges in long-context comprehension, information extraction, and instruction adherence.
• Integrating fragmented risk control knowledge into agent-usable skills

Topic Value:

• Develop agent-based approaches that can adapt and fight new risks on their own, cutting operational costs.
• Improve recall of long-tail and adversarial samples to reduce leakage.

Minimum qualification(s):

• Currently pursuing PhD in Software Development, Computer Science, Computer Engineering, Cybersecurity, or a related technical discipline.

Preferred qualification(s)

• Priority will be given to candidates with good coding skills and a strong command of data structures and fundamental algorithms; For traditional coding roles, proficiency in C/C++ is required; for intelligent coding roles, proficiency in Python is required.
• Priority will be given to candidates with publications in CCF-A category journals or conferences such as AAAI, NeurIPS, SIGKDD, SIGIR, etc.
• Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.