Model Safety Policy Project Intern - AI Data & Safety

The AI Data and Safety team plays a critical role in advancing Seed's foundational models, AI products across modalities, and improving AI-native applications built on the Seed model series. We work across the data lifecycle, from defining evaluation approaches, translating user feedback and benchmark outcomes into data requests, to building scalable processes that improve data quality and support rapid model iteration. Our team combines technical and operational capabilities, bringing together multidisciplinary and multilingual talent across product management, data engineering, and data operations. Our work is driven by people who think deeply about model behavior, move quickly to solve complex problems, and bring first-hand experience as both builders and users of models and agents. In close partnership with internal researchers, industry experts, and leading data vendors, we tackle challenging data problems at the frontier of AI development, helping improve both model performance and user experience. Our team is dedicated to ensuring the safe and ethical development of Seed models by proactively identifying, monitoring, and mitigating risks. Our comprehensive approach includes safety evaluation, safety training, and policy research. We conduct thorough evaluations and red-teaming exercises on Seed models (LLM, VLM, Audio, and Multimodal models) using real-world scenarios to identify and mitigate potential risks. In parallel, we provide hands-on safety training to enhance model behavior and refine our AI safety policy to address emerging risks and ensure compliance with global and regional standards. Ultimately, we drive the development of more responsible AI technologies across the Seed and AI Data & Safety departments. As a Project Intern, you will contribute to impactful short-term projects and gain hands-on experience in a fast-paced, professional environment. This internship offers the opportunity to develop practical skills, apply your knowledge to real-world challenges, and explore your career interests. Applications are reviewed on a rolling basis, so we encourage you to apply early. Your Role Will Involve: 1. Help to conduct research on the latest developments in AI safety across academia and industry, and support the identification of limitations in existing evaluation paradigms. 2. Aid in designing and continuously refining safety evaluation frameworks for multi-models to assess safety-related behaviors, failure modes, and alignment with responsible AI principles. 3. Support projects to enforce safety training or evaluate safety data, which may include data analysis to uncover insights that will inform model iteration and product design improvements. 4. Support the designing, testing, and implementation of automated workflows and methodologies that ensure scalability, efficiency, and coverage within safety contexts, including for safety policy research, safety data generation, data analysis, and evaluation framework validation purposes. Please note that this role may involve exposure to potentially harmful or sensitive content, either as a core function, through ad hoc project participation, or via escalated cases. This may include, but is not limited to, text, images, or videos depicting: - Hate speech or harassment - Self-harm or suicide-related content - Violence or cruelty - Child safety Support resources and resilience training will be provided to support employee well-being. Minimum Qualifications - Currently pursuing Bachelor's degree in AI Policy, Public Policy, Political Science, International Relations, Law, Philosophy, or a related discipline. - Strong analytical skills, with the ability to interpret both qualitative and quantitative data and translate them into clear insights. - Creative problem-solving mindset, with comfort working under ambiguity and leveraging tools and technology to improve processes and outputs. Preferred Qualifications - Experience in AI safety, Trust & Safety, risk consulting, or risk management is highly desirable. - A growth mindset, with a genuine receptiveness and enthusiasm for continuous learning. Readiness to actively solicit and apply constructive feedback. Intellectually curious, self-motivated, detail-oriented, and team-oriented. - Deep interest in emerging technologies, user behavior, and the human impact of AI systems. Enthusiasm for learning from real-world case studies and applying insights in a high-impact setting. - Prior experience building scalable, automated workflows or components of workflows, including hands-on experience with prompt engineering, agent skills, and automated data generation pipelines.