Principal Staff Engineer, AI Platform Research, Data Science (Remote)

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.About the Role:CrowdStrike is looking for a hands-on Staff Data Engineer with deep expertise in Large Language Models (LLMs), Agents and AI platforms to join our growing Data Science Platform Engineering Team. You will be a key technical leader, responsible for designing, building, and deploying cutting-edge data infrastructure that powers our next generation of AI-driven security products. Given the rapid evolution of AI tooling and development standards, we seek a technical leader to stay at the forefront of research, optimizing these systems while driving the platform advancements that accelerate our research teams. This role requires significant hands-on experience in LLM integration, agentic workflows