Ripple
Senior Staff Security Engineer, AI
- Compensation
- Not disclosed
- Location
- San Francisco, CA, United States
- Work setup
- Onsite
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Security
- Posted
Skills
Must have
- 10+ years of Security Engineering experience with demonstrated depth in at least two domains, such as Product Security, Cloud Security, or Security Operations, and meaningful hands-on exposure to AI o
- Solid understanding of AI and LLM security concepts, including prompt injection, jailbreaks, data poisoning, model extraction, RAG manipulation, and agentic risks such as tool poisoning, excessive age
- Experience securing agentic AI systems, including sandboxing, permission scoping, human-in-the-loop design, or runtime monitoring for autonomous workflows
- Fluency with core Security Engineering domains including cloud security on AWS, GCP, or Azure, CI/CD pipeline security, container and Kubernetes security, IAM, and API security
- Strong threat modeling instincts, whether using STRIDE, MITRE ATLAS, OWASP LLM Top 10, or your own approach
- Experience in FinTech, crypto, or other highly regulated environments is a strong plus, ideally with exposure to frameworks like NYDFS, MAS, DORA, or SOC 2
- Proven ability to work across teams, influence technical direction without direct authority, and bring structure to problems that span Engineering, Product, and Security
- A genuine builder's mentality
Full description
You will be Ripple's deepest technical expert at the intersection of artificial intelligence and security. This purpose-built, high-impact individual contributor role spans securing AI systems that Ripple builds and operates, and harnessing AI to make Ripple's security function faster, smarter, and more scalable. You will lead the technical strategy for AI security across the agentic SDLC, define and operationalize guardrails for LLM and agentic AI adoption, and build AI-powered security tooling in close partnership with the broader organization. You will also shape Ripple's external posture on AI security, contributing to industry standards, regulatory discussions, and Ripple's published security practices.
Responsibilities
- Drive the AI Security technical strategy and roadmap, defining how Ripple secures its AI systems, governs agentic workflows, and embeds security controls into the AI development lifecycle
- Design and implement security controls for LLM-integrated and agentic AI systems, including sandboxing, identity and permission scoping, runtime monitoring, and containment of autonomous agent actions
- Own AI security across the Controlled Agentic SDLC, establishing security guardrails, AI provenance standards, dual-review requirements, and audit trail controls
- Lead the security review and risk assessment of all AI integrations entering production, including LLM APIs, SaaS copilots, AI code editors, agentic workflows, third-party MCP servers, and vendor-embedded AI
- Build and scale Ripple's Shadow AI detection capability, surfacing unsanctioned AI usage and driving adoption of the AI acceptable use policy
- Serve as Ripple's go-to technical resource on agentic AI risks, including MCP server security, tool poisoning, prompt injection, and excessive agency in multi-agent systems
- Shape Ripple's external AI security posture, contributing to industry frameworks, engaging regulators, and publishing research
Requirements
- 10+ years of Security Engineering experience with demonstrated depth in at least two domains, such as Product Security, Cloud Security, or Security Operations, and meaningful hands-on exposure to AI or ML security in practice
- Solid understanding of AI and LLM security concepts, including prompt injection, jailbreaks, data poisoning, model extraction, RAG manipulation, and agentic risks such as tool poisoning, excessive agency, and MCP server vulnerabilities
- Experience securing agentic AI systems, including sandboxing, permission scoping, human-in-the-loop design, or runtime monitoring for autonomous workflows
- Fluency with core Security Engineering domains including cloud security on AWS, GCP, or Azure, CI/CD pipeline security, container and Kubernetes security, IAM, and API security
- Strong threat modeling instincts, whether using STRIDE, MITRE ATLAS, OWASP LLM Top 10, or your own approach