Staff Security Engineer, Enterprise AI

Before you apply

Job source

Ats

Applying on

job-boards.greenhouse.io

Workplace

Fully remote

US state

Nationwide remote

Category

IT

Link last checked

7 hours ago

Posted

1 month ago

Closes

No stated closing date

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About the role

At Affirm, we exist for the moments that matter—giving people a clear, predictable way to pay over time, with no hidden fees, no surprises, and no tradeoffs on what matters most. The InfoSec team protects Affirm’s systems and data from evolving threats. We manage security risk, monitor vulnerabilities, and enforce protective controls across the company. The team leads incident response, compliance, identity and access management, and employee training. Our goal is to ensure that security is built into every system and decision at Affirm. We maintain a secure, trustworthy environment so the business can operate and grow with confidence. 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 You will lead and continuously improve Affirm's enterprise AI security review process  evaluating the architecture, data flows, permissions, and design of internal AI tools, agentic/MCP-based systems, and AI features — and embed security requirements into the design phase. You will 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. You will 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. You will 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. You will 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. You will identify emerging classes of AI/agentic security vulnerabilities, develop mitigations before they become incidents, and contribute to AI-specific incident response playbooks as a senior escalation point. You will 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) to translate new research into practical controls. What we look for 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 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 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 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). 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 understand how LLMs and agentic systems are built (RAG, embedding

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