Principal AI Security Researcher

As a Principal Security Researcher, you will: Lead the design and execution of purple team simulations aimed at making AI safer, conducting realistic AI attacks whose findings inform our security products and help them protect customers more effectively. Develop and execute hands-on attacks against AI systems, including: Direct prompt injection and jailbreaks that bypass system prompts, safety instructions, and alignment. Indirect (cross-domain) prompt injection (XPIA), where malicious instructions are hidden in untrusted content the model consumes: documents, web pages, emails, tickets, code, and RAG sources. Multi-turn, crescendo, and social-engineering style attacks that steer a model toward unsafe behavior over a conversation. Multimodal injection through images, audio, and files that carry hidden instructions. Guardrail, content filter, and safety-system bypass, including encoding, obfuscation, and adversarial phrasing. Sensitive data exfiltration and leakage, including system prompt and grounding-data disclosure and cross-tenant or cross-session leakage. Agentic and tool-use abuse: excessive agency, confused-deputy attacks, unsafe autonomous actions, and manipulation of tools, plugins, and connected systems. Data and model attacks: training-data and RAG poisoning, model extraction, model inversion, membership inference, and adversarial examples / evasion. Build scalable AI red teaming tooling and automation (custom attack harnesses and evaluation frameworks) to generate attack variations, run large evaluations, and continuously test AI systems as they change. Partner with product, engineering, Responsible AI, and detection teams to translate findings into mitigations: better system prompts, input and output filters, grounding controls, agent guardrails, and detections. Evaluate the effectiveness of AI defenses (detections, safety classifiers, filters, and monitoring) and provide strategic recommendations to close gaps. Conduct deep research into emerging AI attacker techniques and map them to frameworks such as MITRE ATLAS and the OWASP Top 10 for LLM Applications. Deliver executive-level briefings, technical reports, and prioritized, actionable recommendations. Act as a technical leader: shape AI simulation methodology, mentor team members, and drive long-term innovation in Security for AI. Doctorate in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 3+ years experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection. OR Master's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 4+ years experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection. OR Bachelor's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 6+ years experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection. These requirements include, but are not limited to the following specialized security screenings: Doctorate in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 5+ years experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection. OR Master's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 8+ years experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection. OR Bachelor's Degree in Statistics, Mathematics, Computer Science, Computer Security, or related field AND 12+ years experience in software development lifecycle, large-scale computing, threat analysis or modeling, cybersecurity, vulnerability research, and/or anomaly detection. OR equivalent experience. A security background: experience finding and exploiting real vulnerabilities (for example penetration testing, red teaming, vulnerability research, or application security), with an adversarial mindset. Solid, practical understanding of how modern AI works: LLMs, prompting, retrieval-augmented generation (RAG), fine-tuning, and agentic / tool-using systems. Hands-on experience and familiarity with agentic AI systems, red teaming harnesses, and AI tooling: building or working with agents, tool-using LLMs, orchestration and evaluation frameworks, LLM APIs, and attack harnesses. Demonstrated hands-on experience attacking AI systems: prompt injection (direct and indirect), jailbreaks, guardrail bypass, data exfiltration, or agent abuse, in research or professional settings. Python skills for building attack automation, evaluation harnesses, and research tooling. Familiarity with AI security and safety frameworks such as MITRE ATLAS, OWASP Top 10 for LLMs and Responsible AI principles. Ability to translate offensive findings into practical defenses and to communicate clearly with both engineers and executives. Contributions to AI security research, publications, CTFs, or open-source AI red teaming tooling.