GenAI CBRNE Cyber Expert

Description

As a GenAI CBRNE Cyber Red Team Expert, you will play a critical role in adversarially testing and strengthening the safety guardrails of GenAI systems against high-risk cyber threats involving CBRNE domains.

You will combine deep cybersecurity expertise with CBRNE domain knowledge and hands-on AI red-teaming to design sophisticated adversarial prompts, multi-turn attack scenarios, jailbreaks, and model evaluations. Your work will identify circumstances in which GenAI systems could inadvertently provide information or capabilities that materially enable cyber-enabled CBRNE threats.

The role requires an adversarial mindset: thinking creatively about how malicious actors could manipulate AI systems, combine seemingly benign information across multiple interactions, circumvent safeguards, or exploit model behavior to obtain sensitive cyber-CBRNE information.

Key Responsibilities

• Execute rigorous adversarial red-teaming of Generative AI models across cyber-CBRNE threat scenarios, systematically probing models for vulnerabilities, safeguard bypasses, dangerous capability escalation, and unintended disclosure of sensitive operational information.
• Design and execute adversarial prompts, jailbreaks, prompt mutations, multi-turn conversations, and scenario-based evaluations that test whether model safeguards remain effective against sophisticated or obfuscated cyber-CBRNE requests.
• Develop realistic cyber-CBRNE attack scenarios involving critical infrastructure, industrial control systems, operational technology, cyber-physical systems, laboratory environments, hazardous-material facilities, and other high-consequence systems.
• Evaluate whether models can be manipulated into materially assisting threat actors through attack planning, vulnerability analysis, target-specific reasoning, operational troubleshooting, or the aggregation of individually benign information into higher-risk workflows.
• Identify and document failure modes and attack patterns, including indirect requests, role-playing, encoded or obfuscated prompts, terminology substitution, decomposition of harmful objectives into benign-looking subtasks, and multi-turn escalation.
• Conduct systematic taxonomy audits and safety evaluations, classify model failures by severity and exploitability, reproduce findings, and provide actionable recommendations to AI safety and model-alignment teams.
• Develop repeatable red-team test suites, adversarial datasets, evaluation rubrics, and risk taxonomies for measuring model resilience against emerging cyber-CBRNE threats.
• Maintain current knowledge of emerging GenAI attack techniques, AI safety research, cyber threat intelligence, CBRNE security risks, and cybersecurity threats affecting critical infrastructure and high-consequence environments.

Requirements

Requirements

• Location: Must be located in and authorized to work within the United States (excluding Illinois and Texas) or United Kingdom.
• Education: Advanced degree in Cybersecurity, Computer Science, Engineering, Security Studies, CBRNE-related sciences, or a closely related technical field. Equivalent advanced professional, military, intelligence, government, or industry experience may be considered.
• Cybersecurity Expertise: Deep understanding of cybersecurity concepts, adversarial techniques, vulnerability analysis, attack chains, threat modeling, and defensive security.
• CBRNE Knowledge: Strong understanding of security and risk considerations associated with Chemical, Biological, Radiological, Nuclear, and/or Explosive environments, particularly their intersection with cyber and digital systems.
• Red Team Expertise: Demonstrated experience with red teaming, penetration testing, adversarial simulation, vulnerability research, security testing, threat emulation, or comparable offensive-security methodologies.
• GenAI Red Teaming: Experience or demonstrated aptitude in adversarial prompt generation, jailbreak research, prompt mutation, multi-turn testing, model behavior analysis, and evaluation of LLM safety controls.
• ICS/OT Knowledge: Familiarity with industrial control systems, SCADA, operational technology, cyber-physical systems, or critical-infrastructure environments.
• Adversarial Mindset: Ability to think creatively about how sophisticated users could circumvent model safeguards through decomposition, obfuscation, contextual manipulation, multi-turn interactions, or combinations of otherwise permissible information.
• Communication: Strong technical writing skills with the ability to clearly document prompts, attack methodology, model responses, vulnerabilities, reproduction steps, severity assessments, and recommended mitigations.

Preferred Qualifications

• Hands-on experience with LLM red teaming, AI safety evaluations, jailbreak research, prompt engineering, adversarial prompt writing, or model vulnerability research.
• Experience developing red-team test cases, adversarial datasets, model evaluation benchmarks, attack taxonomies, or automated LLM evaluation pipelines.
• Familiarity with frontier AI safety concepts, dangerous-capability evaluations, responsible scaling frameworks, model safeguards, and emerging GenAI security standards.
• Professional experience in offensive security, penetration testing, vulnerability research, cyber threat intelligence, incident response, or adversary emulation.
• Familiarity with MITRE ATT&CK, MITRE ATT&CK for ICS, NIST cybersecurity frameworks, IEC 62443, and other critical-infrastructure cybersecurity standards.
• Experience working in national security, defense, intelligence, government laboratories, critical infrastructure, CBRNE security, or high-consequence industrial environments.
• Relevant offensive-security, ICS/OT security, or cybersecurity certifications are advantageous.
• Expertise in one or more of the following red-team areas:
• GenAI Cyber Red Teaming: Adversarial testing of LLMs for cyber capabilities, including attempts to circumvent safeguards through prompt manipulation, decomposition, multi-turn interactions, and other adversarial techniques.
• ICS/OT Red Teaming: Security assessment and adversarial testing involving industrial control systems, SCADA, operational technology, cyber-physical systems, and critical infrastructure.
• Chemical & Industrial Cyber Risk: Understanding of cyber threats affecting chemical facilities, hazardous-material environments, industrial processes, process-control environments, and associated safety systems.
• Biological & Laboratory Cyber Risk: Understanding of cyber and information-security risks affecting laboratories, biotechnology environments, research infrastructure, laboratory automation, and associated digital systems.
• Radiological & Nuclear Cyber Risk: Understanding of cybersecurity risks and safeguards associated with nuclear or radiological facilities, monitoring systems, control environments, and supporting infrastructure.
• Cyber-Physical & High-Consequence Threats: Expertise in analyzing scenarios where compromise of digital systems could produce significant physical, safety, environmental, or CBRNE consequences.

The salary range for this role in the US is $150K - $178K - Range may vary based on experience. Salary at the time of offer will be commensurate with experience.

About Alice

Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines.

In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms.

If you're creative and driven to secure the future of AI, we want to hear from you!

Originally posted on Himalayas