Principal Researcher, AI Red Teaming

About the Role

As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We're proud to work for a mission-driven company leveraging AI to transform the way we work. CrowdStrikers drive their careers through flexibility and autonomy while also being expected to contribute to a culture of responsible AI adoption, experimentation, and innovation. We use an AI-first mindset as a force multiplier to proactively and continuously accelerate execution, build expertise, uncover insights, and solve complex problems. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.

What You'll Do

Research (about half of your time): Bring the research subjects. Propose and prioritise the attack classes, agent target classes and frontier model behaviours the team studies, with a written rationale for each and a kill date. Red team frontier models and the agentic systems. Track how each model release shifts attack success, agent behaviour and the controls that hold, and publish the result. Conduct vulnerability research and security assessments on agentic ecosystems end to end: hypothesis, exploit, observed action, disclosure, patch verification. Deliver state-of-the-art research: internal research notes on a monthly cadence, external papers, conference talks and coordinated disclosures with vendor credit. Investigate failure modes personally where the evidence is hardest to get: multi-agent systems, long-horizon tasks, defences that look like they work. Tooling for research and red teaming: Design and write the tooling the research depends on: attack harnesses, action-trace capture, tool-call interception, payload converters, isolated lab targets and reproducible experiment runners. Turn each finding into a reusable attack module with an executing payload test in the team's automated AI security testing platform, and set the engineering bar for how that is done. Define the shared formats for attack content and action traces so red team output can be scored, replayed and reused by the benchmarking work without conversion. Technical guidance (no people management): Own the external research agenda for AI red teaming: which venues, which disclosures, which collaborations, and the quality bar for anything that leaves the team. Review benchmark methodology from the other pillar for attacker realism, and accept review of your own findings for evidence quality. Communicate findings, limitations and risk clearly to engineering, product owners, legal and executives. Report what was not tested as plainly as what was.

What You'll Need

A public record of original security research: CVEs, coordinated disclosures, conference talks or peer-reviewed papers that you led. For this level we expect the record to be substantial and verifiable. Deep hands‑on expertise in offensive security: exploitation, vulnerability research or red teaming, with the ability to find and demonstrate a vulnerability end to end yourself. Demonstrated research on LLMs or agentic systems: prompt and environment injection, tool‑use abuse, jailbreaks, multi‑agent attacks, or model evaluation under adversarial conditions. Shareable artifacts required. Strong Python and systems engineering skills. You write the harnesses, interceptors and converters the team runs on, and you review others' code to a production standard. Practical understanding of how agentic systems are built: tool use, planning loops, MCP, memory, sandboxing, and where trust boundaries sit. Experience setting technical direction for other researchers as a tech lead or principal, with evidence that people you worked with went on to lead work themselves. Exceptional written communication. Your research notes, disclosures and paper are the team's external face.

Bonus Points

Published research on AI security, agent security or adversarial machine learning at recognised venues. Experience red teaming frontier models directly, or working with frontier model providers on disclosures. Patents or shipped tooling in automated red teaming, agent evaluation or security testing. Experience with isolated lab design: sandboxing, egress control, C2 sinkholes, telemetry retention.

#LI-JP2 #LI-Remote

Benefits of Working at CrowdStrike