Staff Security Engineer (Enterprise AI)

• In this role, you’ll build and run Affirms 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 Affirms adopt AI safely, partnering across Security, Legal, Privacy, Compliance, IT, and Engineering to make it scalable and repeatable
• You will lead and continuously improve Affirms 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

Benefits

• Compensation: We have a simple, flexible, and transparent remote‑first compensation structure so you can make the best decisions for yourself and your family
• Spending Wallets: Access tech, food, lifestyle, and family planning wallets for your expenses
• Supportive Communities: Get involved with our employee resource groups and community groups
• Remote‑first Workforce: If your role is remote, you can set up shop anywhere in your home country
• Generous Time Off: Take the time you need when life happens
• Health Benefits: Get a plan that fits your needs
• Mental Healthcare: Take care of your mind with great mental health programs
• Parental Leave: Birth and non‑birth parents get 18 weeks’ paid leave. Plus, a 4‑week return‑to‑work transition program, at full base pay
• Away Days: We offer 20 company‑wide paid days off— which help our teams collectively pause to recharge
• Learning & Development: Engage in exciting learning programs to level up your growth

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 reviewsYou 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 plusYou 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 AWSYou 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 controlsYou 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 fundamentalsYou 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 boundariesYou 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)