Senior Security Engineer (AI Security)

Join Reddit as a Senior Security Engineer, AI Security. In this role, you will help teams build and ship AI-powered products securely. You will review AI-powered product designs, threat model workflows, and build reusable security primitives. You should have strong application security fundamentals, practical understanding of infrastructure security concepts, and experience in product security or security engineering.

• Review and threat model AI-powered product features, LLM integrations, agentic workflows, MCP servers, tools, plugins, retrieval systems, model outputs, and internal AI tools before launch.
• Build reusable AI security primitives such as guardrails, scanners, policy checks, tool-use controls, registries, sandboxes, libraries, and workflow-native enforcement points.
• Partner with teams building products and platforms with AI to define practical security controls that fit how they design, build, and ship.
• The best candidates combine practical application security judgment with strong builder instincts. They can identify risks before launch, then turn repeated findings into guardrails, scanners, registries, sandboxes, libraries, policy checks, or platform controls that scale beyond one product team
• Strong engineering judgment about when to block launch, when to accept risk, and how to sequence practical remediations
• Hands-on experience building security automation, developer tooling, libraries, infrastructure, or platform controls
• Strong application security fundamentals, including secure design review, threat modeling, code review, vulnerability prioritization, and practical remediation
• Practical understanding of infrastructure security concepts such as identity, authorization, network boundaries, secrets, cloud environments, containers, isolation, runtime policy enforcement, and least privilege
• 5+ years of experience in product security, application security, software security, security engineering, backend engineering, or security platform engineering
• Clear communication skills with the ability to explain technical security risk and business impact to engineers, product managers, and leadership
• Ability to reason across trust boundaries, including user input, model context, retrieval systems, backend services, tool calls, MCP servers, third-party integrations, sandboxed execution, logs, and frontend rendering
• Experience in building reliable backend services
• Familiarity with AI, LLM, or agentic system risks such as prompt injection, jailbreaks, insecure tool use, tool poisoning, data leakage, unsafe model outputs, and abuse of AI-assisted workflows
• Experience securing AI/LLM products, AI-assisted development tooling, agent frameworks, MCP-style tool ecosystems, retrieval-augmented generation systems, or model-integrated workflows
• Familiarity with agent sandboxing, workload identity, network policy, tool permissioning, AI red teaming, or LLM evaluation
• Experience building guardrails, policy engines, secure frameworks, scanners, linters, CI/CD checks, registries, gateways, or other developer-facing security platforms
• Experience scanning or governing AI agent components such as skills, prompts, MCP servers, tool manifests, generated code, dependencies, or model-connected workflows
• Familiarity with machine learning systems, model evaluation, AI data flows, or data governance for AI products
• Experience with Go, Python, JavaScript, or TypeScript
• Experience partnering with privacy, trust and safety, infrastructure, platform, or machine learning teams
• Hands-on experience securing distributed systems or cloud-native applications, including Kubernetes, APIs, and microservices
• Track record of mentoring engineers or raising the security bar through guidance, tooling, or reusable patterns