An LLM Red Teamer, also known as a GenAI Red Teamer, tests how large language models and generative AI products behave when someone tries to make them misbehave. The focus is the model's responses and the instructions around it. Can a user talk the model out of its policies? Can a document it retrieves take control of the conversation? Will it reveal its system prompt, other users' data or content it was trained to refuse?
Organisations hire LLM red teamers because generative models fail in ways that conventional software testing does not catch. Outputs are probabilistic, behaviour shifts with every fine-tune or prompt change, and the attack surface is natural language. A dedicated red teamer gives product, policy and safety teams a structured view of those failures before customers or journalists find them.
What does an LLM Red Teamer do?
Most of the work is creative, systematic probing backed by careful record keeping. An LLM Red Teamer builds a taxonomy of the harms and failure modes relevant to the product, attacks the model against each one, and turns successful attacks into test cases that can be replayed later. The role often spans pre-release testing of new model versions and ongoing testing of live products.
- Crafting jailbreaks using role play, persona switching, multi-turn escalation, obfuscation and encoding tricks
- Testing direct and indirect prompt injection, including payloads hidden in web pages, emails, files and retrieved documents
- Attempting system prompt extraction and leakage of personal or confidential data from context windows and memory features
- Probing policy boundaries for harmful content, bias, misinformation and unsafe advice in specialist domains
- Testing multimodal inputs where images or audio carry hidden instructions
- Converting successful attacks into regression suites and automated evaluations for each model release
- Reviewing guardrails, refusal behaviour and content classifiers, and reporting where they are too weak or too strict
Key skills and backgrounds
LLM red teamers come from a wider range of backgrounds than most security roles. Some are security engineers who have specialised in GenAI. Others come from trust and safety, computational linguistics, ML evaluation or policy research, and bring deep knowledge of how harms show up in language. For red teaming in specialist areas such as chemistry, biology, cyber or finance, domain experts are often paired with technical red teamers.
The common thread is a methodical mindset. Good candidates document every attempt, separate one-off flukes from repeatable failures, and can explain why an attack works.
- Strong understanding of how LLMs are trained and aligned, including instruction tuning and RLHF, and why those methods leave gaps
- Prompt engineering at an adversarial level, across single-turn and multi-turn attacks
- Python for scripting attacks, calling model APIs at scale and analysing results
- Building evaluation datasets and harnesses, including automated attack generation with one model attacking another
- Familiarity with the OWASP guidance on LLM application risks, MITRE ATLAS and the NIST AI RMF
- Knowledge of content policy design and harm taxonomies
- Clear, neutral writing for reports read by engineers, policy staff and leadership
Where LLM Red Teamers work
Foundation model labs employ LLM red teamers in-house and often bring in external specialists for pre-release testing. Companies building products on top of third-party models, such as customer service assistants, coding tools, search products and enterprise copilots, hire them to test their own prompts, retrieval layers and guardrails. Trust and safety teams at consumer platforms are adding GenAI red-teaming capacity, and AI assurance firms and security consultancies hire LLM red teamers to deliver client engagements.
LLM Red Teamer vs related roles
LLM red teaming is one part of a wider offensive AI security field. These are the closest neighbours.
- An Adversarial ML Engineer studies attacks on models at the level of gradients, training data and model internals, while an LLM Red Teamer mostly works through the model's inputs and outputs.
- An AI Penetration Tester assesses the full application, including APIs, authentication and infrastructure, and treats the LLM as one component among many.
- A Prompt Injection Researcher goes deeper on a single attack class, developing new injection and jailbreak techniques and testing defences against them.
Salary and market data
Pay for LLM red teamers varies with technical depth, domain expertise and whether the role is in-house or consulting. Our GenAI and agentic systems salary guide covers current ranges, the AI and deep tech salary guides give wider context, and the deep tech salary calculator helps you benchmark a specific offer.
Hiring LLM Red Teamers with DeepRec
DeepRec places specialists in GenAI and agentic systems across Europe and the US, from offices in London, Berlin, Zurich, Dublin and Boston. We screen LLM red teamers technically, asking them to walk through attacks they have built, how they recorded them and how they judged severity, so you meet people with real testing experience. We recruit on a permanent, contract, retained and executive search basis, including short contract engagements for pre-release testing. To discuss a hire, go to Looking to Hire.