Security Engineer to AI Engineer
Your Skills Transfer.

More than you'd expect. Threat modeling translates directly to AI risk assessment. Red team experience is invaluable for LLM jailbreak testing and prompt injection research. Your systems thinking helps with AI infrastructure security. The adversarial mindset—thinking like an attacker—is exactly what's needed for AI safety and robustness testing. Companies are desperate for people who understand both security AND AI.

The field is exploding. Key areas include: prompt injection prevention and detection, jailbreak research and red teaming, AI guardrails implementation, model access control and authentication, data poisoning detection, AI supply chain security, and compliance (EU AI Act, emerging regulations). Companies like Anthropic, OpenAI, and every enterprise deploying AI need these skills. Most AI engineers have zero security background—you have a massive advantage.

Not for AI security roles. You need to understand how LLMs work conceptually—tokenization, attention, context windows, fine-tuning—but not the mathematical derivations. Focus on practical skills: prompt engineering, API security, guardrail implementation, and red teaming techniques. The deep ML math is for researchers building models, not for securing them.

For security engineers, 3-6 months is realistic for an AI security role. You're not starting from zero—you're adding AI context to existing skills. Month 1-2: Learn LLM fundamentals and how models work. Month 2-4: Deep dive into AI security specifics (prompt injection, jailbreaks, guardrails). Month 4-6: Build portfolio projects and start applying. Your security experience accelerates this significantly compared to a typical career changer.

Look for: AI Security Engineer, LLM Red Team Engineer, AI Safety Engineer, ML Security Specialist, Prompt Security Engineer, or AI Trust & Safety roles. Some companies call it AI Risk or AI Governance. Also consider AI Engineer roles at security-focused companies—your background becomes a differentiator. Don't limit yourself to 'AI Security' in the title; many AI Engineer positions desperately need security thinking.

Start with understanding how LLMs actually work—take a practical course like Andrej Karpathy's 'Neural Networks: Zero to Hero' or fast.ai. Then explore OWASP LLM Top 10 and research on prompt injection (Simon Willison's blog is excellent). Build a project: try red teaming an open-source LLM or building guardrails. Document everything—your security-informed perspective on AI vulnerabilities is valuable content.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.