Offensive AI Security Engineer – Red Team
Job Title: Offensive AI Security Engineer – Red Team
We are seeking an Offensive AI Security Engineer to join our AI Red Team within the Security Engineering team. This role focuses on adversarial machine learning (ML), AI-driven offensive security, and red teaming AI systems to uncover vulnerabilities in AI-powered automotive security models and vehicle platforms.
As part of Lucid’s Offensive AI Security team, you will attack, manipulate, and exploit AI/ML models to identify real-world threats and weaknesses in AI-driven security solutions. You will develop AI-enhanced security automation tools, perform LLM-based penetration testing, and integrate AI/ML attack techniques into offensive security operations.
Key Responsibilities:
AI Red Teaming & Adversarial Attack Development
- Design and execute adversarial attacks on AI-powered security systems.
- Conduct LLM-based penetration testing to uncover AI security flaws in vehicle cybersecurity applications.
- Identify attack surfaces in AI-driven perception systems (LiDAR, Radar, Cameras) and develop exploits against automotive AI models.
AI-Driven Offensive Security Automation
- Build tools and systems to analyze emerging AI threats in vehicle security and enterprise AI models.
- Develop AI-assisted security automation tools for:
- Reconnaissance – Automate vulnerability discovery using LLMs and RAG-based intelligence gathering.
- Exploitation – Use AI to generate attack payloads and automate offensive security operations.
- Fuzzing – Enhance automated fuzz testing with AI-driven input mutation strategies.
- Reverse Engineering – Apply LLM-assisted binary analysis for rapid security assessments.
- Build offensive and defensive AI security tools, integrating ML-driven automation into security assessments and exploit development.
ML-Driven Security Research & Exploitation
- Use ML to characterize a program, identifying security-critical functions and behavioral anomalies.
- Understand LLVM Intermediate Representation (LLVM IR) to analyze compiled AI/ML software for security weaknesses.
- Develop AI-driven techniques for:
- Threat Detection – Use ML to automate malware detection and anomaly recognition.
- Cryptographic Algorithm Classification – Identify cryptographic weaknesses in compiled binaries.
- Function Recognition – Use AI models to automate binary function analysis and decompilation.
- Vulnerability Discovery – Automate zero-day discovery using ML-based exploit prediction models.
- Evaluate ML security models for robustness, performance, and adversarial resilience.
Offensive AI Research & Red Teaming Strategy
- Research novel AI attack techniques and evaluate their impact on vehicle cybersecurity and enterprise AI security models.
- Collaborate with internal Red Teams, SOC analysts, and AI security researchers to refine AI-driven offensive security approaches.
- Stay ahead of emerging AI threats, tracking advancements in AI security, adversarial ML, and autonomous vehicle AI exploitation.
Required Skills & Qualifications:
AI Red Teaming & Offensive Security Expertise
✔ Hands-on experience with AI/ML exploitation, adversarial ML, and AI-driven pentesting.
✔ Strong background in attacking AI models, including LLMs, deep learning systems, and computer vision AI.
✔ Experience with LLM-based vulnerability analysis, prompt engineering attacks, and model evasion techniques.
✔ Proficiency in penetration testing, AI fuzzing, and red teaming AI-driven security applications.
Cybersecurity & AI Security Research
✔ Experience in exploiting AI-powered vehicle security mechanisms.
✔ Ability to analyze, test, and exploit AI models within embedded systems and cloud environments.
Programming & Security Tooling
✔ Strong Python, C, C++ skills for AI security research and offensive security automation.
✔ Proficiency with AI security frameworks (TensorFlow, PyTorch, Hugging Face, OpenAI APIs, LangChain).
✔ Hands-on experience with reverse engineering tools (Ghidra, IDA Pro, Binary Ninja, Qiling, Angr).
✔ Familiarity with AI fuzzing tools (AFL++, Honggfuzz, Syzkaller) and adversarial ML frameworks (CleverHans, Foolbox, ART).
Preferred Qualifications:
- Bachelor’s degree in Cybersecurity, AI/ML, Computer Science, or related technical field(s) is required. Master’s degree or higher education is preferred.
- Experience in offensive AI security, red teaming AI models, or AI-powered security automation.
- Prior work in LLM-based security analysis, AI-driven pentesting, and adversarial ML research.
Base Pay Range (Annual)
$128,800 - $177,100 USD
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