AI Security Research Engineer II (Full Time) - (United States)

Cisco is a technology company focused on connecting and protecting organizations in the AI era. The AI Security Research Engineer II will conduct applied AI security research, threat-model AI systems, build guardrails and automated security checks, perform adversarial evaluations, and partner with engineering and product teams to enable secure AI adoption.

Responsibilities:

• Executes straightforward, short-term AI security initiatives with guidance (e.g., one- to three-month deliverables such as a new guardrail, a GitHub Action security check, or an evaluation harness)
• Influences a single feature or product team, helping them reach a secure outcome faster with AI, not despite it
• Applies a working understanding of AI/ML security concepts and frameworks (OWASP LLM Top 10, OWASP Agentic Top 10, MITRE ATLAS, NIST AI RMF) and develops judgment on ethical and responsible-AI considerations
• Supports AI-specific threat models for AI-native features (RAG, tool use, agentic workflows, MCP (Model Context Protocol) integrations), including risks such as prompt injection (direct and indirect), jailbreaks, data poisoning, model extraction, excessive agency, and supply-chain risk
• Runs defined adversarial tests and evaluations using Cisco AI Defense (Cisco's AI security solution) alongside open-source tooling such as NVIDIA's Garak; documents findings, success rates, and remediation guidance
• Builds or contributes to security tooling and guardrails (input/output filtering, automated checks, and release gates), delivered in CI/CD pipelines and agentic workflows under direction
• Integrates AI security into existing Secure Development Lifecycle practices: threat modeling, SAST/DAST/SCA, dependency and supply-chain, secrets, and licensing
• Supports data protection requirements for AI systems, including data classification, minimization, residency, and sensitive data handling
• Supports human review, approval, and accountability controls for AI-assisted development and agentic workflows
• Contributes to AI security standards, patterns, and security requirements, treating reference architectures as living documents that absorb emerging threats and engineering feedback
• Develops and tests hypotheses about AI attack surfaces and control effectiveness; uses measurable evidence rather than manual assertions
• Shares findings and demos with the team and partner groups with guidance; interacts with peers to understand cross-functional security needs
• Contributes to reusable patterns, training material, office hours, and security champion enablement for engineering teams
• Contributes to literature reviews, threat research write-ups, and presentations shared within the company; participates in internal and external AI security talks and discussions
• Develops familiarity with the fast-moving AI/LLM platform, agent-framework, and attacker-technique landscape through hands-on experimentation

Requirements:

• Bachelor's + 2 years of related experience, or Master's + 0 years of related experience
• Foundation in security engineering (application/product security, secure SDLC, threat modeling) and/or AI/ML engineering, with demonstrated adversarial thinking
• Proficiency in Python
• Hands-on exposure to LLM security: prompt injection defense, guardrails, insecure output handling, RAG and agentic-system risks
• Familiarity with AI red-teaming/eval tooling, including Cisco AI Defense and an open-source framework such as NVIDIA's Garak
• Experience with CI/CD, MCP (Model Context Protocol), agent-to-agent integration patterns, and AI development tools (Cursor, Claude Code, Copilot)
• Knowledge of OWASP LLM/Agentic Top 10, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, or the EU AI Act
• A portfolio of applied work (security tooling contributions, CTF participation, or published threat research) is valued over certifications