Applied AI Security Engineer

Accountabilities

• Audit how AI tools and MCP integrations are being used across the organization, identifying connections to sensitive systems, data, credentials, and services to inform security policies.
• Design and implement layered AI security guardrails, including system-level prompts, scoped permissions, sandboxing approaches, and other controls that constrain AI behavior.
• Build and maintain an inventory of MCP servers, AI-to-service connections, and associated data-access permissions, with clear visibility into what each integration can access and why.
• Partner directly with engineers and business users to embed secure-by-default practices into AI-assisted workflows and ensure security guidance translates effectively into day-to-day usage.
• Assess new AI tools, plugins, integrations, and access requests, identifying practical and secure ways to enable adoption.
• Continuously test, challenge, and red-team AI security controls to uncover vulnerabilities, bypasses, and gaps before they are exploited.
• Create clear documentation, security patterns, and guidance that can be understood and applied by audiences without a dedicated security background.

Requirements

• Bachelor's degree in Computer Science, Software Engineering, Cybersecurity, or a related technical field.
• Professional software development experience with strong fundamentals in services, APIs, authentication, authorization, permissions, and system integrations.
• Hands-on experience using AI coding or productivity tools such as Claude, GitHub Copilot, Gemini, or comparable solutions in business-critical workflows.
• Working knowledge of core security principles, including least privilege, sandboxing, trust boundaries, and foundational threat modeling.
• Strong communication and collaboration skills, with the ability to work alongside engineers and technical teams as a trusted security partner.
• Curiosity about emerging AI technologies and a practical understanding of their capabilities, limitations, and security implications.
• Experience with MCP (Model Context Protocol) or similar tool-calling and agent-integration frameworks is a strong plus.
• Familiarity with AI/LLM attack techniques such as prompt injection, jailbreaking, or related adversarial methods is advantageous.
• Experience in autonomous vehicles, robotics, or other safety-critical technology environments is a bonus.

Benefits

• US salary range: $139,000–$258,000 USD per year.
• Canada salary range: $118,000–$168,000 CAD per year.
• Competitive compensation package with discretionary equity incentive awards and annual performance bonuses.
• Medical, dental, and vision coverage, along with broader health and wellness benefits.
• Unlimited vacation.
• Flexible working hours and work-from-home support.
• Daily drinks, snacks, and catered meals when working from the office.
• Regular team-building activities and social events.
• An evolving benefits and perks program within a rapidly growing technology environment.
• Opportunities to work on advanced AI and autonomous technology challenges with a collaborative, innovative team.

How Jobgether Works

• We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements.
• Their system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company.
• The final decision and next steps (interviews, assessments) are managed by their internal team.

Why Apply Through Jobgether?

• Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer.
• This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR).
• You may exercise your rights (access, rectification, erasure, objection) at any time.
• We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information.
• These tools assist our recruitment team but do not replace human judgment.
• Final hiring decisions are ultimately made by humans.