AI Red Team Engineer

What AI Red Teaming Is

Red teaming, in traditional security, means simulating real-world attacks against an organization's systems to identify vulnerabilities before actual adversaries do. Red teams think like attackers. They attempt to breach defenses, escalate privileges, exfiltrate data, and disrupt operations using the same techniques that malicious actors would use.

AI red teaming applies this same adversarial approach to AI and machine learning systems. Instead of (or in addition to) attacking networks and applications, AI red team engineers attack models, training pipelines, inference systems, and AI-powered products. The goal is the same: find the vulnerabilities before the adversaries do.

The practice gained momentum after Microsoft established the Microsoft AI Red Team (MART) in 2018, making it one of the first dedicated AI red teams in industry. Since then, Google, Meta, OpenAI, Anthropic, and the US government (through NIST and the AI Safety Institute) have all established or expanded AI red teaming capabilities.

How AI Red Teaming Differs From Traditional Red Teaming

The core mindset is the same: think adversarially, find weaknesses, report findings so defenders can fix them. But the attack surface is fundamentally different.

Traditional red teaming targets: network infrastructure, web applications, physical security, social engineering vectors, endpoint security, Active Directory environments, and cloud configurations.

AI red teaming targets: model behavior (jailbreaks, prompt injection, adversarial examples), training data integrity (poisoning, bias injection), model weights and intellectual property (extraction, theft), AI-powered product features (automated actions, content generation), safety properties (harmful output generation, discrimination, privacy violations), and supply chain integrity (malicious model files, compromised dependencies).

The tooling is also different. Traditional red teams use tools like Metasploit, Burp Suite, and Cobalt Strike. AI red teams use adversarial ML libraries (Counterfit, ART, TextAttack), custom prompt injection payloads, model probing scripts, and evaluation frameworks designed for AI systems.

Day-to-Day Work

Pre-Release Model Evaluation

Before a new AI model or product feature ships, the red team conducts an adversarial evaluation. For a new LLM, this means systematic testing of jailbreak resistance across thousands of attack prompts, checking for unintended information disclosure, testing safety guardrails across languages and domains, and evaluating how the model handles adversarial inputs designed to cause harmful outputs. Red team engineers design the test methodology, build automated testing frameworks, manually probe for edge cases, and write reports that inform the go/no-go decision for deployment.

Ongoing Adversarial Research

AI attack techniques evolve rapidly. Red team engineers stay current on new adversarial research, replicate published attacks against their company's systems, and develop novel attack techniques. This research often leads to published papers and conference presentations, making the role attractive to engineers who value intellectual contribution.

Attack Simulation Exercises

Red team engineers conduct realistic attack simulations that test the full defensive chain: Can an attacker manipulate training data through a compromised data source? Can adversarial inputs bypass the content safety system? Can model weights be extracted through the API? These exercises test not just the AI systems but also the detection, monitoring, and incident response capabilities of the security organization.

Tooling Development

AI red teaming requires specialized tools that often do not exist as off-the-shelf products. Red team engineers build automated testing frameworks for adversarial evaluation, custom payload generators for prompt injection, model behavior analysis tools that detect safety regressions, and benchmarking systems that track model robustness over time.

Skills Required

AI red teaming requires a specific combination of offensive security and ML expertise.

• Offensive security fundamentals: Penetration testing methodology, vulnerability research, exploit development. OSCP or equivalent experience is common.
• ML/AI knowledge: Understanding of model architectures, training processes, and inference behavior. Ability to read and apply adversarial ML research.
• Programming: Strong Python skills are essential. Experience with ML frameworks (PyTorch, Hugging Face) and adversarial ML libraries (Counterfit, ART).
• Research skills: Ability to read academic papers, replicate published attacks, and develop novel techniques. Many AI red team roles have a research component.
• Communication: Ability to write clear vulnerability reports and present findings to engineering and leadership teams. Red teaming is only valuable if the findings lead to fixes.

Companies With AI Red Teams

The following companies have established AI red teams or are actively building them:

Company	Team Name	Focus Area
Microsoft	MART (Microsoft AI Red Team)	Copilot, Azure OpenAI, internal AI systems
Google	AI Red Team	Gemini, Vertex AI, internal AI infrastructure
Meta	Purple Team (AI focused)	Llama models, content integrity AI
OpenAI	Security/Safety Team	GPT models, ChatGPT, API platform
Anthropic	Safety/Security Team	Claude models, API security

Compensation

AI Red Team Engineer roles command premium compensation, typically ranging from $170,000 to $290,000 in total cash compensation at major tech companies and frontier AI labs. The premium over traditional red teaming reflects the scarcity of professionals who combine offensive security expertise with ML knowledge. Equity at pre-IPO AI companies can add significant additional value.

How to Get Into AI Red Teaming

The most common path is from traditional red teaming or penetration testing, with added ML expertise. Build adversarial ML skills through CTF competitions (Gandalf, AI Village at DEF CON), open-source tool contributions (Counterfit, Garak), and published research. Companies with established AI red teams value candidates who demonstrate both offensive security track record and genuine understanding of AI systems.

An alternative path is from ML engineering with added offensive security skills. This works particularly well at frontier AI labs (OpenAI, Anthropic) where understanding model internals is as important as knowing how to exploit them.