Research Scientist - Risk Control Vertical LLM Foundation and Agent - Global Tech Research Program - 2027 Start (PhD)

Responsibilities

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.

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

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

Qualifications

Minimum Qualification(s):
1. Individuals who are completing or recently completed a PhD in Software Development, Computer Science, Computer Engineering, Cybersecurity, or a related technical discipline.

Preferred Qualification(s):
1. Excellent coding skills and a solid foundation in data structures and algorithms; proficiency in Python is required, and familiarity with PyTorch or TensorFlow (TF) is preferred.
2. Outstanding ability to define, analyze, and solve problems; candidates with publications in CCF-A category journals or conferences such as AAAI, NeurIPS, SIGKDD, SIGIR, etc., are preferred.
3. Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.