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). About the Team:
Nestled Flow AI Organization, the Security AI team stands as a beacon of innovation, exploring cutting-edge technology to enhance the security of Large Language Models (LLMs) and their applications serving the company's global products.
Tasked with constructing, implementing, and sustaining secure frameworks, the team pioneers a new frontier in AI Security Research, inviting talented individuals with a background in AI to join through the student researcher program.
Through collaboration and unwavering commitment, this team ensures a safe and secure digital experience for users worldwide while offering a stimulating environment for LLM enthusiasts to thrive and shape the future of AI security.
Topic Content:
While agent-based applications are experiencing explosive growth, they have also introduced new security challenges.
The expanded attack surface covering data, decision-making, execution, and supply chains, combined with their intricate interactions and complex infrastructure, has rendered traditional security technologies ineffective.
This topic aims to systematically research adversaial testing and evaluation technologies for foundation models and agents in the Agentic AI era, as well as a full-link defense system based on foundation model trusted privacy computing, to ensure the safe and stable development of the company's business related to foundation models, including:
1. Attack-defense detection and evaluation methods, testing benchmarks, and toolchains for multimodal foundation models and agents;
2. Risk defense technologies and trustworthy runtime security assurance for agent-based applications;
3. Training and inference technologies for foundation models and samples, ensuring data and model asset security;
4. Secure construction, performance optimization, and of confidential training and inference infrastructure for foundation models.