AI Red Teamer
Proactively attacks AI systems — especially LLMs and generative AI — through adversarial simulation to discover security vulnerabilities and harmful outputs before deployment. Formalized by the 2023 Biden AI Executive Order requiring red-teaming results. Lower barrier to entry — CTF rankings and published research matter more than years of experience. WEF: only 14% of organizations have adequate AI security talent.
- Salary Range
- $110K–$200K
- Hiring Companies
- OpenAI · Anthropic · Google DeepMind
- Hot Regions
- USAU.K.IsraelSingaporeAustralia
- Core Skills
- Security testing · Adversarial attacks · Model evaluation
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Job Description Analysis
Core Responsibilities
- Design and execute adversarial attack tests on LLM and generative AI systems to discover security vulnerabilities and harmful outputs
- Develop automated red team testing tools and prompt injection detection framework
- Write a security assessment report, classify the discovered vulnerabilities according to risk levels and make recommendations for repairs
- Collaborate with the model safety team to verify the effectiveness of Safety Guardrails
- Track the latest AI attack techniques (Jailbreak, Prompt Injection, Indirect Attacks) and update testing methods
Requirements
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- Deep understanding of the security risks of LLM: Prompt Injection, Jailbreak, data leakage, harmful output generation
- Master automated security testing tools (Garak, PyRIT, Counterfit) and adversarial sample generation technology
- Have a network security or penetration testing background and be familiar with the OWASP Top 10 for LLM
- Python programming ability, able to develop custom attack scripts and evaluation tools
- Excellent security analysis and report writing skills
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Nice to Have
- CTF competition experience or active contribution to the security community
- Published research papers or technical blogs related to AI security
- Previous experience working in traditional AppSec or Red Team
A Day in the Life
- Morning: Use automated tools to run the Jailbreak test suite on the new version of the model and collect attack success rate data
- Morning: Manual testing of newly discovered Indirect Prompt Injection attack vectors
- Afternoon: Write a security assessment report of last week’s red team testing, including vulnerability ranking and remediation priority
- Afternoon: Discuss the newly launched Safety Filter with the security team and verify its protective effect against known attacks.
- Evening: Research newly disclosed AI vulnerabilities in the security community and update the internal attack knowledge base
Career Transition Guide
Suitable Backgrounds
- Network security engineer/penetration tester: direct migration of offensive and defensive thinking, the fastest way to get startedSee this career today: Cybersecurity Engineer · Cyber Security Analyst
- Security researcher: academic research ability and vulnerability discovery thinking are highly matched
- QA/Test Engineer: Systematic testing thinking can be transformed to supplement security attack and defense knowledgeSee this career today: QA
- Curious Developers: CTF Enthusiasts and Bug Bounty Hunters Have a Natural Advantage
Transition Path
- Step 1: Learn the OWASP Top 10 for LLM Applications and understand the security risks unique to AI
- Step 2: Practice in public AI red team challenges (e.g. HackAPrompt, Gandalf)
- Step 3: Learn to use automated red team testing frameworks such as Garak and PyRIT
- Step 4: Publish AI security research (blogs, papers, or vulnerability reports) to build influence in the security community
- Step 5: Apply for an AI Red Team position at an AI lab or a large tech company
Learning Roadmap
Phase 1: Security Basics (January-February)
Phase 2: Tools and Technologies (February-March)
Phase 3: Professional improvement (3-6 months)
Common Mistakes
Only test simple Jailbreak prompts and ignore more subtle indirect attacks
Indirect Prompt Injection (via external data injection) is often more dangerous than direct Jailbreak
Only focus on model output security and ignore data leakage and privacy risks
AI red team testing should cover four dimensions: output security, data leakage, system integrity, and usability.
Think you need an in-depth ML research background to get into the industry
Practical ability is more important than academic qualifications - CTF rankings and open source contributions are better credentials for entry into the industry
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