AI Application Security Engineer

Our Mission Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model. Why Now Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services. Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact. You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now. About the Role As our Security Engineer, Application & AI, you will own the security of our products and application layer — secure development practices, agent security, third-party integration security, and data protection for AI products operating in some of the world's most regulated and sensitive environments. This is a hands-on builder role. You will write code, ship se [... source excerpt omitted ...] application layer. This role is specifically designed to address that surface, working alongside the Infrastructure Security Engineer who owns the platform layer underneath. What You'll Work On Application Security Own secure development practices across our products: AuthN/AuthZ patterns, secrets management, input handling, and secure-by-default standards that engineers can follow without security becoming a bottleneck. Integrate security into the development lifecycle — code review, CI/CD pipelines, and pre-deployment checks — catching risk before it reaches production. Conduct threat modeling across product features and release cycles, translating risk into concrete con [... source excerpt omitted ...] ies are built. Produce security artifacts for agent features and product deployments: threat models, architecture reviews, and documentation that supports delivery into regulated customer environments. Data Protection Define and enforce data protection standards at the application layer — ensuring sensitive customer data (PHI, PII, government records) is handled correctly as it flows through AI pipelines and surfaces in agent outputs. Build safeguards against unauthorized data exposure across our products: access controls, output filtering, and audit logging that make data handling attributable and reviewable. Design secure data handling patterns for AI features operating on