2 months ago
Base Salary
$191k - $267k/yr
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 in inference, retrieval, and execution paths to detect and prevent prompt injection, jailbreaks, tool misuse, data leakage, unsafe code generation, and suspicious agent behavior.
- Partner with product and platform teams to define practical security controls for AI-enabled systems.
- Find, fix, and prevent AI security issues while making required engineering changes clear and low-friction for partner teams.
- Convert one-off AI security issues into systemic fixes, paved paths, measurable controls, and reusable guidance.
Requirements
- At least 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 and security automation, developer tooling, libraries, infrastructure, or platform controls.
- Familiarity with AI, LLM, or agentic system risks, including 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 involving user input, model context, retrieval systems, backend services, tool calls, MCP servers, third-party integrations, sandboxed execution, logs, and frontend rendering.
- Practical understanding of identity, authorization, network boundaries, secrets, cloud environments, containers, isolation, runtime policy enforcement, and least privilege.
- Strong judgment in evaluating launch risk and sequencing practical remediations, along with clear communication of technical risks and business impact.
- Preferred experience securing AI or LLM products, AI-assisted development tooling, agent frameworks, MCP-style ecosystems, retrieval-augmented generation systems, or model-integrated workflows.
- Preferred experience building guardrails, policy engines, secure frameworks, scanners, linters, registries, gateways, or developer-facing security platforms.
- Preferred familiarity with agent sandboxing, workload identity, network policy, tool permissioning, AI red teaming, LLM evaluation, machine learning systems, model evaluation, or AI data governance.
- Experience with Go, Python, JavaScript, or TypeScript is preferred.
- Preferred experience securing distributed systems or cloud-native applications, including Kubernetes, APIs, and microservices.
- Track record of mentoring engineers or raising security standards through guidance, tooling, or reusable patterns.
Benefits
- Remote role, as indicated by #LI-Remote.
- U.S.-based employees may receive medical, dental, and vision insurance, a 401(k) program with employer match, generous vacation time, and parental leave.
- Interviews may be recorded, transcribed, and summarized by artificial intelligence, with an option to opt out before scheduled interviews.
Tech Stack
About Reddit
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