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AI Red Team Engineer
Three components - Automation Resistance, Structural Moat, and Demand - add up to 51.
Federal labor data does not count AI red-team engineers separately; the wage, workforce, openings, and AI-exposure numbers use Information Security Analysts as the public comparison. That fits the security base, but AI model-evaluation and release-review jobs are a narrower specialty.
Automation resistance comes from threat judgment and severity calls, while routine test generation is easy to accelerate. AI can multiply prompt variants and organize failures, so resistance comes from threat design, severity judgment, and the ability to force fixes.
Substitution resistance is limited for simple prompt testing, but stronger when the work designs realistic attacks and judges severity.
Augmentation leverage is high because AI can generate variants, organize failures, and help write repeatable tests.
The moat is security credibility, reproducible findings, and engineering trust rather than a legal credential. The practical barrier is security credibility: reproducible reports, model-evaluation skill, engineering trust, and a history of helping teams remediate. The reports need practical fixes attached.
Physical and environmental protection is minimal because the work is digital and remote-friendly.
Regulatory pressure can help when buyers require documented model testing, but there is no protected license.
Robotics are not the substitute path; the role is about security reasoning and model behavior.
Credential depth is moderate through security experience, certifications, technical portfolios, and model-evaluation proof.
Demand has a strong security backdrop, but AI red-team roles are a specialty inside the broader information-security market. Hiring rides on the information-security market plus model-evaluation contracts, lab safety work, procurement scrutiny, and release review.
Volume is supported by strong information-security demand, though the AI red-team specialty is smaller than the parent row.
Source quality is decent because information security analysts are a relevant public anchor, but not an exact specialty count.
Resilience is fair because threat activity, procurement scrutiny, and model-release risk keep demand from depending only on hype.
The case weakens if vendors automate large libraries of adversarial prompts and employers treat red-team work as a cheap pre-release checklist. That would compress entry roles that do not include security reasoning or remediation authority. That would turn shallow portfolios into weak evidence, because the market would expect deeper attack reasoning.
The case strengthens if major customers, insurers, or regulators require independent model testing before deployment. That would create steadier demand for people who can document risks, reproduce failures, and verify fixes. Buyers would then need workers who can compare claims, verify fixes, and explain residual risk without hype.
A mixed outcome needs review if security-focused red teams grow while general safety prompt testing becomes temporary work. The important signal would be whether the role connects findings to product changes and accountability. The career risk is ending up in a narrow testing lane that never reaches security architecture or product authority.