Job Description

Team Introduction
The AI Data and Safety team plays a critical role in advancing Seed foundational models across modalities and improving AI-native applications built on the Seed model series. We work across the data lifecycle, from defining evaluation approaches and 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 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 Seed 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.

AI Data & Safety - External Partnership Development Team is dedicated to continuously sourcing high-quality data suppliers for foundation models and AI-native applications. Based on the data requirements of model training, the team conducts market research and manages the end-to-end process—from supplier sourcing and evaluation, commercial negotiations, and project initiation, to final delivery. By efficiently fulfilling data requests and building a robust supplier ecosystem, the team provides a reliable, high-quality data foundation for the continuous iteration of our models.

AI Training Data Market Research and Resource Development
1. Partner closely with AI model training teams to understand business needs, define data types, standards, and core requirements, and conduct structured industry and market research to stay abreast of AI data trends, market landscape, and technological advancements
2. Translate market insights into high-quality data resource development strategies by identifying and engaging startups and leading data suppliers; establish effective supplier evaluation and screening mechanisms; and expand and onboard suppliers to build a robust, high-quality data ecosystem
3. Conduct strategic analysis of market, supplier, and industry data to extract actionable insights; produce analytical reports to support decision-making in AI model training, data procurement, and business strategy
4. Continuously identify and monitor emerging data resources and potential partners, maintain strong market relationships, and ensure access to forward-looking, high-quality data sources

Supplier Onboarding, Delivery, and Lifecycle Management
1. Lead supplier onboarding, including evaluation, qualification review, and capability validation, ensuring alignment with project delivery standards and seamless integration into internal workflows
2. Manage end-to-end commercial engagements with suppliers, including contract negotiation, terms drafting, cost control, and compliance review, ensuring efficient and compliant partnerships while optimising commercial terms
3. Oversee supplier delivery across projects by coordinating internal and external stakeholders, clarifying requirements (even in ambiguous scenarios), managing expectations, and ensuring timely, high-quality data delivery aligned with project goals
4. Maintain and develop long-term supplier relationships; regularly evaluate supplier performance and service quality; optimise supplier management frameworks; and leverage data analysis to generate insights and improve supplier performance and collaboration value