Senior Security Engineer, AI Security

About This Opportunity

We are representing a confidential social technology platform in their search for a Senior Security Engineer, AI Security. Our client operates one of the internet's most visited community platforms and is actively building AI-powered products that require sophisticated security engineering.

This role combines product security judgment with hands-on engineering to secure AI systems, tools, and workflows. You will review AI-powered product designs, threat model LLM and agentic workflows, and build reusable security primitives that make secure AI development easier for engineering teams. This is not a machine learning engineering role, but you should be comfortable reasoning about how AI systems fail, how agents use tools, and how security controls fit into inference, retrieval, tool-use, and execution paths.

The ideal candidate combines practical application security judgment with strong builder instincts—someone who can identify risks before launch, then turn repeated findings into guardrails, scanners, registries, sandboxes, libraries, policy checks, or platform controls that scale beyond a single product team.

Responsibilities

• 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
• Design security tooling that can sit in the inference, retrieval, or execution path to detect and prevent prompt injection, jailbreaks, tool misuse, data leakage, unsafe code generation, and suspicious agent behavior
• Partner with teams building AI-powered products and platforms to define practical security controls that fit how they design, build, and ship
• Proactively find, fix, and prevent AI security issues while making any required product or engineering changes clear and low-friction for partner teams
• Turn one-off AI security issues into systemic fixes, paved paths, measurable controls, and reusable guidance

What We're Looking For

Required Qualifications:

• 5+ years of experience in product security, application security, software security, security engineering, backend engineering, or security platform engineering
• Strong application security fundamentals, including secure design review, threat modeling, code review, vulnerability prioritization, and practical remediation
• Experience building reliable backend services
• Hands-on experience building security automation, developer tooling, libraries, infrastructure, or platform controls
• 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
• 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
• Practical understanding of infrastructure security concepts such as identity, authorization, network boundaries, secrets, cloud environments, containers, isolation, runtime policy enforcement, and least privilege
• Strong engineering judgment about when to block launch, when to accept risk, and how to sequence practical remediations
• Clear communication skills with the ability to explain technical security risk and business impact to engineers, product managers, and leadership

Preferred Qualifications:

• Experience securing AI/LLM products, AI-assisted development tooling, agent frameworks, MCP-style tool ecosystems, retrieval-augmented generation systems, or model-integrated workflows
• Experience building guardrails, policy engines, secure frameworks, scanners, linters, CI/CD checks, registries, gateways, or other developer-facing security platforms
• Familiarity with agent sandboxing, workload identity, network policy, tool permissioning, AI red teaming, or LLM evaluation
• 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

How We Work With You

When you apply through Get A Job.ai, our recruiting team will review your profile and schedule an initial screening call to learn about your background and career goals. If there's a strong match, we'll prepare and submit your candidacy to our client for consideration. Throughout the interview process, we'll provide guidance and support to help you succeed.

Important: Please apply only through Get A Job.ai and work exclusively with our recruitment team. Do not contact the client directly, as this may disqualify your application.

Pay

Compensation details will be discussed during the screening process with our talent team.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices and welcomes candidates from all backgrounds. We provide equal employment opportunities regardless of race, color, religion, sex, national origin, age, disability, veteran status, or any other protected characteristic.