How to Evaluate Candidates for AI Red Teaming Roles

AI red teaming is one of the fastest-growing disciplines in cybersecurity, and hiring for it is genuinely difficult. The role sits at the intersection of offensive security, machine learning, prompt engineering, and responsible AI governance. Candidates who excel in one area often lack depth in another, and because the field is still maturing, there is no established pipeline of experienced practitioners to draw from.

This guide walks you through a structured process for evaluating candidates for AI red teaming roles, from the initial screen through to a final hiring decision. Follow these steps and you will build a consistent, defensible process that surfaces the right people rather than the most confident interviewees.

Skills and background to screen for first

Before you schedule a single interview, define what a qualified candidate actually looks like for your specific context. AI red teaming roles vary significantly depending on whether the organization is defending its own AI systems, stress-testing third-party models, or advising clients on AI risk. The baseline skills you screen for should reflect that reality.

At the screening stage, look for evidence of the following across CVs, portfolios, and any written submissions:

• Offensive security fundamentals: Experience with adversarial thinking, penetration testing methodology, or vulnerability research. Candidates do not need to come from a pure red teaming background, but they must understand how attackers reason.
• Working knowledge of large language models and AI systems: Familiarity with how models are trained, fine-tuned, and deployed. This does not require a research background, but candidates should be able to speak fluently about model behaviour.
• Hands-on experience with adversarial AI techniques: Prompt injection, jailbreaking, data poisoning, model inversion, or evasion attacks. Look for evidence they have actually attempted these, not just read about them.
• Scripting and automation ability: Python proficiency is the practical minimum. Candidates who can automate attack scenarios at scale are significantly more effective in this role.
• Documentation and reporting: AI red teamers must communicate findings clearly to non-technical stakeholders. Look for writing samples, published research, or reports if available.

Use a structured screening scorecard at this stage. Assign each criterion a weight based on your role requirements and score every candidate against the same rubric. This prevents the natural tendency to advance candidates who present well on paper but lack the technical substance you actually need. With your shortlist established, the next step is building an assessment that tests these skills under realistic conditions.

Design a technical assessment for AI attack scenarios

A well-designed technical assessment is the most reliable signal you will get during the entire hiring process. The goal is not to trick candidates but to observe how they approach a problem they have not seen before, which is precisely what AI red teamers do on the job.

Structure the assessment around a realistic scenario rather than abstract questions. Give candidates access to a sandboxed environment with a deployed AI system, such as a simple chatbot or a fine-tuned language model, and ask them to attempt to elicit harmful, misleading, or unintended outputs. Keep the scope realistic for the time available, typically two to four hours for a take-home assessment.

1. Define the target system clearly: provide documentation about the model’s intended use, any known constraints, and the deployment context.
2. Set explicit objectives: ask candidates to identify at least three distinct attack vectors, document their methodology, and propose mitigations for each finding.
3. Require a written report: the report should be readable by a non-technical stakeholder and should clearly explain the risk posed by each finding.
4. Include one deliberately ambiguous scenario: introduce a situation where the ethical path forward is not obvious, and observe how the candidate navigates it.

After candidates submit their assessments, evaluate them against a shared rubric before reading any identifying information. Score for technical depth, creativity of attack approach, quality of documentation, and the appropriateness of proposed mitigations. A candidate who finds fewer vulnerabilities but documents them with precision and proposes thoughtful fixes is often more valuable than one who generates a long list of findings without actionable insight.

Probe depth of knowledge in the interview

The technical interview should build on the assessment rather than repeat it. By this point you already have evidence of what the candidate can do independently. The interview is your opportunity to understand how they think, how they handle uncertainty, and how deep their knowledge actually goes beneath the surface.

Open by asking the candidate to walk you through their assessment submission. Listen for how they describe their reasoning, not just their findings. Strong candidates will explain why they chose a particular attack vector before explaining how they executed it. They will acknowledge what they did not find and speculate about why.

From there, use follow-up questions to probe the boundaries of their knowledge:

• Ask them to explain a technique they used in their assessment as if speaking to a product manager with no security background.
• Present a novel attack scenario they have not seen and ask them to reason through it aloud. You are not looking for the right answer; you are watching their process.
• Ask what they would do if they discovered a critical vulnerability in a client’s production AI system during an engagement. How would they handle disclosure?
• Explore their awareness of emerging AI attack surfaces: multimodal models, agentic AI systems, retrieval-augmented generation pipelines. Candidates do not need to be experts in all of these, but they should demonstrate curiosity and awareness.

Avoid questions with a single correct answer. The best AI red teamers operate in genuinely ambiguous territory, and your interview questions should reflect that. If a candidate seems uncomfortable with uncertainty or defaults quickly to textbook answers without engaging with the nuance of the question, treat that as a meaningful signal. Those who are exploring cybersecurity opportunities in this space tend to be driven by intellectual curiosity as much as technical skill.

Assess collaboration and communication skills

AI red teaming is rarely a solo activity. Practitioners work alongside AI engineers, legal and compliance teams, product managers, and executive stakeholders. A candidate who cannot translate technical findings into language that drives decisions will create friction regardless of how skilled they are at the attack side of the role.

Build a structured communication exercise into your process. Ask the candidate to take one of their assessment findings and present it verbally to you, role-playing as a non-technical stakeholder. Observe whether they:

• Lead with business impact rather than technical detail
• Use analogies effectively without oversimplifying
• Invite questions and adapt their explanation in response
• Remain confident without becoming dismissive of concerns

Also probe for how they handle disagreement. Ask about a time they identified a risk that a stakeholder did not want to act on. How did they respond? Did they escalate, document, or accept the decision? There is no universally correct answer, but the response tells you a great deal about how the candidate will operate within your organization’s culture and risk appetite.

Finally, ask about their experience working in cross-functional teams. AI red teaming increasingly requires close coordination with the people who build and maintain the systems being tested. Candidates who have worked alongside ML engineers or data scientists, even informally, tend to ramp up faster and generate more actionable findings because they understand the system from the inside.

Validate ethical judgment and responsible AI awareness

Ethical judgment is not a soft skill in AI red teaming. It is a core professional requirement. Practitioners regularly encounter situations where the technically interesting path and the responsible path diverge, and the decisions they make in those moments have real consequences.

Do not assess ethics through abstract hypotheticals. Instead, present candidates with realistic dilemmas drawn from the kinds of situations AI red teamers actually face:

1. A client asks you to test whether their model can be manipulated into producing content that could harm a specific demographic. How do you approach the engagement?
2. During a red team exercise, you discover that the model you are testing is leaking personally identifiable information from its training data. The engagement scope did not include data privacy testing. What do you do?
3. You find a high-severity vulnerability but the client’s legal team advises against documenting it in writing. How do you respond?

Listen for candidates who engage seriously with the complexity of each scenario rather than jumping to a pat answer. Strong candidates will acknowledge competing obligations, think through the implications of different courses of action, and demonstrate awareness of frameworks like responsible disclosure and AI safety principles without needing to be prompted. Candidates who treat ethics as a box-ticking exercise rather than a genuine professional commitment are a risk in a role with this level of access and influence.

Score and compare candidates consistently

Inconsistent evaluation is one of the most common failure modes in technical hiring. When different interviewers assess candidates against different implicit standards, the final decision often reflects who advocated most loudly in the debrief rather than who was actually the strongest candidate. A structured scoring process prevents this.

Before the process begins, align your hiring panel on a shared scorecard that covers each evaluation dimension. Assign a numerical weight to each dimension based on your role requirements. A typical weighting for an AI red teaming role might look like this:

• Technical depth and adversarial skill: highest weight, assessed through the technical assessment and interview
• Communication and documentation quality: significant weight, assessed through the assessment report and communication exercise
• Ethical judgment and responsible AI awareness: significant weight, assessed through scenario-based questions
• Collaboration and cross-functional experience: moderate weight, assessed through behavioural interview questions
• Learning agility and curiosity: moderate weight, assessed throughout all stages

After each interview, have every panel member complete their scorecard independently before the group debrief. This prevents anchoring, where the first opinion shared in a debrief disproportionately shapes everyone else’s view. During the debrief, focus the conversation on evidence rather than impressions. If a panel member scores a candidate low on ethical judgment, they should be able to point to a specific response that informed that score.

Compare finalists side by side using their aggregate scores, and document the reasoning behind your final decision. This creates accountability, supports fair hiring practices, and gives you a reference point if a similar role opens in the future. If you are hiring for cybersecurity roles regularly, this documentation also helps you refine your process over time based on how placements perform.

How Iceberg supports AI red team hiring

Building a rigorous evaluation process is only part of the challenge. Finding candidates worth evaluating in the first place is where most organizations get stuck. AI red teaming sits at a narrow intersection of skills, and the pool of experienced practitioners is small relative to demand.

This is where we can help. At Iceberg, we specialize in placing elite cybersecurity professionals, including those operating in emerging disciplines like AI red teaming. Here is what we bring to the process:

• A global network of over 120,000 cybersecurity professionals across 23 countries, giving you access to candidates who are not actively browsing job boards
• Deep domain knowledge that allows us to pre-qualify candidates against the technical and cultural criteria that matter for your specific role
• Speed without compromise: 98% of our placements remain in their roles or are promoted within 18 months, reflecting the quality of our matching process
• A complimentary Vacancy Health Check for organizations struggling to fill AI red teaming or broader cybersecurity positions, providing a clear diagnosis and actionable recommendations

If you are ready to move forward, get in touch with our team to discuss your hiring needs. Whether you need one specialist or are building out an entire red team function, we can help you find the right people faster.