AI & Data Architect - Trust and Safety

The AI & Data Architect will define the AI and data foundation that enables Trust & Safety (T&S) to scale decision intelligence, automation, and operational excellence. This role will bridge AI, data, product, engineering, and safety teams to translate complex safety challenges into scalable solutions. The architect will shape technical strategy, establish reusable architecture patterns, and enable responsible adoption of AI across T&S.

Responsibilities: Define AI & Data Architecture Strategy: - Establish the target-state architecture for T&S data and AI capabilities, including data foundations, knowledge systems, AI platforms, and intelligent workflows. - Develop scalable architecture principles, standards, and reusable patterns across T&S teams. - Partner with cross-functional leaders to identify high-impact opportunities where AI and data can improve safety outcomes.

Build Scalable AI & Data Foundations: - Design AI-ready data models, data products, and architecture patterns to support analytics, machine learning, and GenAI applications. - Enable integration of diverse safety data sources, including policy knowledge, enforcement decisions, investigations, user reports, and operational signals. - Architect next-generation AI solutions using approaches such as RAG, knowledge systems, AI agents, and human-in-the-loop workflows. - Drive solutions from experimentation to reliable production by improving scalability, reliability, explainability, and governance.

Drive Technical Leadership & AI Transformation: - Act as a design authority for strategic AI and data initiatives across Trust & Safety. - Create reference architectures and best practices for responsible AI adoption. - Influence engineering, data science, product, and operations teams to accelerate AI-enabled safety transformation. - Mentor teams and raise overall architecture maturity across T&S.

Minimum Qualification(s): - 5+ years of experience in data architecture, AI systems, machine learning platforms, or related technical domains. - Strong experience designing scalable data ecosystems and AI-enabled applications. - Deep understanding of data architecture, machine learning lifecycle, GenAI architecture patterns (e.g., RAG, embeddings, knowledge systems), and responsible AI practices. - Ability to translate ambiguous business problems into scalable technical solutions and influence across technical and non-technical stakeholders.

Preferred Qualification(s): - Experience building AI systems in large-scale consumer platforms. - Experience in Trust & Safety, fraud prevention, cybersecurity, risk, or compliance domains. - Familiarity with ML platforms, feature systems, vector databases, knowledge graphs, or MLOps.