Senior Security Engineer, AI Vulnerability Management

Robinhood Robinhood · Fintech · Menlo Park, CA +1 · Security Division

Senior Security Engineer focused on AI Vulnerability Management. The role involves architecting and deploying AI agents for automated security tasks like vulnerability triage, threat intelligence correlation, and remediation generation. It also includes building systems to model exploitability, creating automated security guardrails, and developing LLM-powered dashboards for security insights. The goal is to create a self-scaling, intelligence-driven defense system.

What you'd actually do

  1. Set Strategic RBVM Vision: Act as the technical lighthouse, defining the multi-year roadmap and driving the move toward Risk-Based Vulnerability Management (RBVM), prioritizing vulnerabilities based on real-world exploitability and business context.
  2. Architect Agentic AI Systems: Design and deploy AI agents that autonomously triage findings, correlate threat intelligence, and generate production-ready remediations (e.g., automated Pull Requests for dependency updates and config drift).
  3. Build Exposure Intelligence: Develop systems that correlate vulnerabilities with runtime context and infrastructure topology (Kubernetes/AWS) to accurately model real-world blast radius and ensure engineers only fix what is actually exploitable.
  4. Automate Triage & Self-Healing: Create "paved roads" and CI/CD guardrails that prevent specific vulnerability categories from ever reaching production, reducing manual toil for the entire engineering organization.
  5. Data-Centric Visibility: Build high-fidelity dashboards using LLM-powered summarization to translate complex security signals into actionable insights for engineering leadership.

Skills

Required

  • 5+ years in Security Engineering with a track record of leading high-impact automation or security platform initiatives at a Senior or Staff level.
  • Hands-on experience building or deploying agentic systems or LLM orchestration frameworks (e.g., LangChain, AutoGPT) to solve complex security or engineering problems at scale.
  • Strong software engineering background with proficiency in Go or Python and a history of building scalable, API-driven security tooling.
  • Deep knowledge of securing AWS and Kubernetes-based architectures.
  • High familiarity with vulnerability categories, exploitability, and modern risk frameworks (CVSS, EPSS, CISA KEV).

Nice to have

  • Experience navigating security in highly regulated or high-growth financial environments.
  • Experience implementing "Security as Code" within large-scale CI/CD environments.

What the JD emphasized

  • AI Vulnerability Management
  • Agentic AI
  • Machine Learning
  • AI agents
  • LLM-powered summarization
  • Agentic System Fluency
  • building or deploying agentic systems or LLM orchestration frameworks

Other signals

  • Leveraging Agentic AI and Machine Learning to automate the discovery, prioritization, and remediation of risk at scale
  • Design and deploy AI agents that autonomously triage findings, correlate threat intelligence, and generate production-ready remediations
  • Build high-fidelity dashboards using LLM-powered summarization to translate complex security signals into actionable insights