Staff Security Engineer (Enterprise AI)

Join Affirm as a Staff Security Engineer (Enterprise AI) and lead the end-to-end security review process for enterprise AI/LLM systems. You will evaluate architecture, prioritize AI-specific risks, and design controls to ensure safe AI adoption. Collaborate with various teams, threat model AI systems, review source code, and design security guardrails. You will also evaluate third-party AI capabilities, identify emerging vulnerabilities, and lead cross-functional AI security initiatives. Enjoy a remote-first work environment, generous time off, and comprehensive health benefits.

• Lead and continuously improve Affirm's enterprise AI security review process, evaluating architecture, data flows, permissions, and design of internal AI tools.
• Threat model AI/LLM-based systems and their data flows for risks, drive remediation, and review source code, system prompts, and configurations.
• Design and build security guardrails and tooling for AI systems, enforce and automate AI security, and evaluate AI capabilities of third-party SaaS vendors.

• You understand how LLMs and agentic systems are built (RAG, embeddings, fine-tuning, tool use) and authn/authz models (OAuth2, SAML, service-account/non-human identities) for agentic and machine-to-machine access, with strong application-architecture and threat-modeling fundamentals
• You have practical experience threat modeling and reviewing AI/LLM applications (e.g., against the OWASP Top 10 for LLM Applications) and securing agentic systems and tool-calling frameworks — MCP servers/clients, tool-permission models, and agent-to-tool trust boundaries
• You can lead cross-functional initiatives across Security, Engineering, Legal, Privacy, and Compliance and drive them to closure, and communicate effectively with technical and executive audiences. Experience in regulated environments (SOC 2, PCI DSS) and applying IAM to non-human/agent identities is a plus
• You have built AI governance artifacts (acceptable use policy, data-handling standards, vendor/model risk assessments) and evaluated AI capabilities within SaaS platforms (e.g., Notion AI, Slack AI, Google Workspace AI, GitHub Copilot) as part of vendor reviews
• You can build security tooling, guardrails, and detections with Python or similar, and deploy cloud services and policy-as-code using Infrastructure as Code (Terraform or similar); familiarity with Kubernetes and AWS
• You are a seasoned security engineer with hands-on experience designing, evaluating, and maintaining security architecture for AI/LLM-based systems, plus deep expertise in enterprise security systems, processes, and controls
• You have experience with enterprise tools for AI visibility and control (e.g., CASB, IDP/Okta) and familiarity with the corporate systems where AI is adopted (OpenAI, Anthropic, GitHub, Google Workspace, Slack, Notion, Jira)