How to apply for Head of Cyber Safety, Red Team

About Gray Swan

Gray Swan is a pioneering AI security company focused on automatically assessing the risks of AI models. Their mission is to ensure the safe deployment of frontier AI systems by proactively identifying vulnerabilities. Working here means being at the forefront of AI safety, collaborating with top talent in a fast-paced, mission-driven environment.

About the role

As the Head of Cyber Safety, Red Team, you will lead Gray Swan's efforts to evaluate frontier AI systems for offensive cyber risks. This role is pivotal in designing adversarial evaluations, building scalable defenses, and shaping the future of AI security. You'll bridge the gap between cybersecurity and machine learning, making a direct impact on the safety of AI technologies.

A typical day

A typical day might involve leading a team meeting to review ongoing red-team evaluations, collaborating with ML engineers to refine benchmarks, and diving into technical analysis of a new LLM's cyber capabilities. You might also spend time mentoring team members, writing reports on findings, and staying abreast of the latest AI security research.

Who Gray Swan is looking for

• Deep offensive cybersecurity expertise, with hands-on experience in vulnerability discovery, exploit development, and red teaming.
• Proven experience in AI red-teaming or LLM security evaluation, understanding how to probe AI models for harmful capabilities.
• Strong leadership skills, with experience building and mentoring teams of cybersecurity experts in ambiguous environments.
• Comfortable working at the intersection of cybersecurity research, AI safety, and machine learning, with the ability to partner with ML engineers.

Tips for this application

• Highlight specific examples of adversarial evaluations you've designed for LLMs or other AI systems, including methodologies and outcomes.
• Showcase your experience in translating cybersecurity expertise into automated benchmarks or guardrails, as this is a core part of the role.
• Demonstrate your ability to work in ambiguous environments by describing projects where you defined processes from scratch.
• Emphasize any experience with frontier AI models (e.g., GPT-4, Claude) and specific vulnerabilities you've discovered or mitigated.
• Tailor your resume to include keywords like 'red teaming', 'LLM security', 'adversarial machine learning', and 'autonomous operations' to pass ATS.

What to cover in your cover letter

["Express your passion for AI safety and why Gray Swan's mission resonates with you.", 'Detail your offensive cyber expertise and how it applies to evaluating AI systems for cyber risks.', 'Highlight your leadership experience and vision for building a cyber safety team.', 'Mention your ability to collaborate with ML engineers and contribute to scalable defenses.']

Research before applying

• Read Gray Swan's website and any published research or blog posts to understand their tools and methodologies.
• Look into their recent projects or partnerships to see how they assess AI risks.
• Familiarize yourself with the broader AI safety landscape, including organizations like Anthropic, OpenAI, and other red-teaming initiatives.
• Review common LLM vulnerabilities (e.g., prompt injection, jailbreaks) and current defense mechanisms.

Likely interview topics

Based on the job description, expect questions about:

• How would you design an adversarial evaluation to test an LLM's ability to autonomously discover vulnerabilities?
• Describe a time you identified a critical vulnerability in an AI system. What was your approach?
• How do you stay updated on the latest developments in AI security and offensive techniques?
• How would you balance the need for thorough testing with the risk of enabling misuse?
• What strategies would you use to build and mentor a team of cybersecurity experts in a remote-first environment?

Common mistakes to avoid

• Don't focus solely on traditional cybersecurity without connecting it to AI/LLM-specific risks.
• Avoid being vague about your experience; provide concrete examples and metrics.
• Don't underestimate the importance of collaboration with ML engineers; emphasize your ability to work cross-functionally.