careers in test

Adversarial AI Tester (AI Red Teamer)

“Have you ever wondered what it would be like to have a job where your sole purpose is to outsmart, bypass, and intentionally break advanced AI applications before malicious actors can? That’s the role of an Adversarial AI Tester.”

An Adversarial AI Tester (or AI Red Teamer) is an offensive security and software quality specialist focused entirely on discovering systemic flaws, safety risks, and security vulnerabilities within GenAI features, LLMs, and agentic workflows. Unlike deterministic software testing, they simulate real-world attacker techniques, crafting targeted exploitation paths to bypass application alignment filters, protect data privacy, and enforce robust model behavior.

Knowledge Required
  • OWASP Top 10 for Large Language Applications vulnerabilities (e.g., prompt injection, data poisoning, model theft).
  • Common AI jailbreaking paradigms (e.g., role-based manipulation, multi-turn payload delivery, cryptographic evasion).
  • Socio-technical risk domains (e.g., toxic content production, algorithmic bias, PII leakage, misinformation propagation).
  • Enterprise security baselines and security architectures (e.g., NVIDIA’s Secure AI Framework and Google’s SAIF).
  • Core underlying machine learning mechanics (tokenization limits, context-window mechanics, and decoding parameters).
Skills Required
  • Creative, unconventional problem-solving and an adversarial mindset.
  • Python scripting fluency for automated payload generation and parallelized prompt injection.
  • Expertise in mapping testing flaws to structured security taxonomies (e.g., MITRE ATLAS framework).
  • Ability to cleanly translate complex model failure logs into reproducible datasets for developer engineering streams.
  • Excellent technical risk communication across cross-functional engineering, legal, and non-technical business units.
Typical Responsibilities
  • Designing and executing comprehensive, manual and automated red team campaigns against user-facing AI endpoints.
  • Crafting specialized string payloads and input chains to evaluate the resilience of application guardrails and filter middleware.
  • Annotating, categorizing, and scoring structural failure modes to assist in downstream model alignment (RLHF/DPO remediation).
  • Authoring transparent, highly detailed reports that provide actionable mitigation guidance for developer infrastructure.
  • Continually mapping, researching, and reverse-engineering the latest public open-source exploitation frameworks and emerging AI vectors.
Common Tools

Garak, PyRIT (Python Risk Identification Tool), Inspect, Promptfoo, Burp Suite, Metasploit, Jupyter Notebooks

Connect & Facilitate

This role forms a crucial bridge between Cybersecurity Teams, DevSecOps, and AI Product Development units, fusing defensive IT quality controls directly with bleeding-edge penetration testing methodologies.

Rate Table (National Average)

Note: This is a premium specialization tier requiring a strong overlap of cybersecurity expertise and machine learning data comprehension, resulting in elevated contractor and permanent rates.

RemunerationValue
Daily Rate (contract)$1,100 – $1,400
FTE Salary (Permanent)$160,000 – $210,000

Project Hiring Cost (average)

These percentages are derived from an annualized amount. Given the costs involved in sourcing, vetting, and correspondence for a role of this type, a recruiter would expect a minimum fixed fee of 15K, although most recruiters operate on percentages nowadays.

Project Hiring CostValue
Internal HR18-22%
Recruiters25%

Interview Questions

Here are some interview questions you will most likely encounter for this role. While we don’t provide answers, we do clarify the intent behind the questions, which makes them a great resource when researching the role in readiness for an interview.

To test the candidate’s understanding of systemic data flow entry points and how a model treats unverified external data as instructions.

To gauge programmatic creativity and practical experience in forcing system alignment targets to collapse.

To ensure the tester can clearly tie abstract model failure states directly back to corporate liability and tangible business impacts.

To test deep technical knowledge of discrete optimization vulnerabilities, assessing whether the candidate can think beyond manual conversational roleplay into algorithmic exploitation.

To evaluate the candidate’s ability to execute defense-evasion strategies, specifically assessing their understanding of how to blind side a defensive model using nesting or semantic obfuscation.

ATS Keyphrases

These keywords are commonly used by recruiter Application Tracking Systems to determine the relevance of a CV or cover letter to a specific position description. By ensuring at least a few of these key phrases appear throughout your CV and cover letter, you increase your relevance where an ATS is being used.

AI Red Teaming, Adversarial AI Testing, Prompt Injection Defense, Jailbreak Exploitation, OWASP LLM Top 10, MITRE ATLAS Framework, PyRIT, Garak, Model Privacy Attacks, Data Poisoning Remediation, Input Filtering Vulnerability, Information Evasion, Indirect Prompt Injection, Vulnerability Taxonomies, Penetration Testing, Offensive Security Testing, AI Safety Guardrails, Model Extraction Prevention, Token Payload Engineering, Socio-Technical Risk Identification