Senior Software Engineer - AI Security

Requirements

• We’re not looking for just a coder — we want a builder with ownership and first-principles thinkingwho:
• Is comfortable building end-to-end products(UI, APIs, systems, deployment)
• Thinks from first principles, not just patterns or frameworks
• Demonstrates high agency— takes ownership, drives clarity, and executes without waiting
• Writes clean, scalable, maintainable codewith strong fundamentals
• Has experience or strong interest in AI-powered development(LLMs, agents, automation, copilots)
• Uses AI tools to accelerate development and decision-making
• Can operate in low-structure, fast-moving environmentswith minimal PM/design dependency
• Makes pragmatic trade-offs — balances speed, quality, and long-term thinking
• Cares about shipping real products, not just building components
• Years of experience does not matter to us, but we think most devs would need 4+ years of engineering experienceacross frontend + backend
• Interest and knowledge of security is a plus

What the job involves

• We’re looking for a Senior Software Engineer who can own end-to-end product development — from idea to production, for our AI security team. You’ll build and ship AI-powered products (LLM-driven workflows, automation, intelligent systems) across the stack — frontend, backend
• This is a high-agency, low-process role. You won’t wait for specs. You’ll define problems, make decisions, and move fast with strong engineering judgment. Your work will directly shape how we build, scale, and accelerate software using AI
• Own end-to-end product development— from idea, research, and architecture to production
• Build and ship AI-powered security productsacross frontend, backend, and APIs
• Develop scalable, high-quality interfaces and systemsthat power real user workflows
• Work with minimal specs — define problems, make decisions, and execute independently
• Collaborate when needed, but primarily operate with lean ownership and high autonomy
• Drive adoption and impact— iterate based on real usage, not just requirements
• Leverage LLMs and AI toolingto accelerate development and improve team productivity
• Continuously improve systems, code quality, and developer workflows