Responsibilities

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. Your Role Will Involve: - Conduct research on the latest developments in AI safety across academia and industry. Proactively identify limitations in existing evaluation paradigms and propose novel, defensible approaches to test and evaluate models under real-world and edge-case scenarios. - Design and continuously refine safety evaluation guidelines for multi-models. Define and implement robust evaluation metrics to assess safety-related behaviors, failure modes, and alignment with responsible AI principles. - Conduct thorough analyses of safety evaluation results to surface safety issues stemming from model training, fine-tuning, or product integration. Translate these findings into actionable insights to inform model iteration and product design improvements. - Partner with cross-functional stakeholders to build scalable safety evaluation workflows. Help establish feedback loops that continuously inform model development and risk mitigation strategies, and work closely with other operations teams to ensure high-quality, safety-aligned judgments, evaluation outcomes, and synthesized training data. - Manage end-to-end project lifecycles, including scoping, planning, execution, and delivery. Effectively allocate team resources and coordinate efforts across functions to meet project goals and timelines. - Continuously innovate on the creation and design of new 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.

Qualifications

Minimum Qualifications - Bachelor's degree or higher, preferably in AI Policy, Public Policy, Political Science, International Relations, Law, Philosophy, or a related discipline. - Strong command of English in both written and oral communication. - Strong analytical skills, with the ability to interpret both qualitative and quantitative data and translate them into clear insights. - Proven project management abilities, with experience leading cross-functional initiatives in dynamic, fast-paced environments. - Creative problem-solving mindset, with comfort working under ambiguity and leveraging tools and technology to improve processes and outputs. Preferred Qualifications - Professional experience in AI safety, Trust & Safety, Risk consulting, or Risk management. Experience working at or with AI companies is highly desirable. - Prior experience building scalable, automated workflows, including hands-on experience with prompt engineering, agent skills, and automated data generation pipelines. - 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. - Technical background that aids in the designing of automated workflows (e.g., coding) or technical knowledge of generative AI technologies are advantageous but not essential.

Model Safety Analyst - AI Data & Safety