Staff Security Engineer, Enterprise AI

About the Role

In this role, you'll build and run Affirm's end-to-end security review process for enterprise AI/LLM systems evaluating architecture, prioritizing AI-specific risks, and designing the controls and guardrails that let Affirm adopt AI safely, partnering across Security, Legal, Privacy, Compliance, IT, and Engineering to make it scalable and repeatable.

What You’ll Do

• Lead and continuously improve Affirm's enterprise AI security review process evaluating architecture, data flows, permissions, and design of internal AI tools, agentic/MCP-based systems, and AI features — and embed security requirements into design.
• Threat model AI/LLM-based systems and their data flows for risks such as prompt injection, insecure output handling, excessive agency, tool-permission abuse, data poisoning, and sensitive-data exposure, and drive remediation.
• Review source code, system prompts, agent configurations, and tool/permission manifests (e.g., MCP definitions), and help tool owners build security-focused test cases and red-team/eval scenarios to verify requirements before launch.
• Design and build security guardrails and tooling for AI systems permission boundaries, authn/authz for agentic tools and MCP servers, data-handling controls, logging/monitoring, and policy-as-code (Python, IaC) to enforce and automate AI security.
• Evaluate the AI capabilities of third-party SaaS vendors (e.g., Notion, Slack, Google Workspace) as part of vendor and SaaS security reviews and drive risk-based adoption decisions.
• Identify emerging classes of AI/agentic security vulnerabilities, develop mitigations before incidents occur, and contribute to AI-specific incident response playbooks as a senior escalation point.
• Lead cross-functional AI security initiatives to closure, advise technical and executive stakeholders as an internal point of expertise, and stay current on the AI security landscape (OWASP LLM Top 10, MITRE ATLAS).

What We Look For

• Hands-on experience designing, evaluating, and maintaining security architecture for AI/LLM-based systems, plus deep expertise in enterprise security systems, processes, and controls.
• Practical experience threat modeling and reviewing AI/LLM applications (e.g., against 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.
• Experience building AI governance artifacts (acceptable use policy, data-handling standards, vendor/model risk assessments) and evaluating AI capabilities within SaaS platforms as part of vendor reviews.
• Experience with enterprise tools for AI visibility and control (e.g., CASB, IDP/Okta) and familiarity with corporate systems where AI is adopted (OpenAI, Anthropic, GitHub, Google Workspace, Slack, Notion, Jira).
• Ability to 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.
• Strong understanding of 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.
• Ability to lead cross-functional initiatives across Security, Engineering, Legal, Privacy, and Compliance, communicating 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.