Requirements

B.S. or M.S. in Computer Science, Cybersecurity, or related field, or equivalent experience. OR PhD + 2 years of hands-on security engineering experience
5+ years of hands-on security engineering experience in one or more domains: detection engineering, threat intelligence, incident response, cloud security, adversary simulation, offensive security, or digital forensics
Extensive knowledge of attacker tactics, techniques, and procedures
Proficiency in coding with experience in languages such as Python, Go, C/C++, or shell scripting
Experience building security systems, tools, or capabilities — not just identifying problems but engineering solutions
Demonstrated ability to operate independently on complex, ambiguous security challenges

Nice to Haves

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience leveraging AI tools (LLMs, agents, orchestration systems) to accelerate security workflows and enhance operational capability
Contributions to the security community (original research, tools, CTF design, conference presentations, publications)
Experience creating structured methodologies that scale security expertise across teams
Experience planning and executing adversary simulation campaigns or purple team exercises at scale
Experience with cloud security operations (AWS/GCP/Azure), cloud detection and response, or cloud-native defense
Experience with forensic investigation — reconstructing attack timelines from evidence across multiple sources
Background in supply chain security, mobile platform security, network protocol security, or AI/agent security
Experience improving AI model performance through expert feedback, red-teaming, or evaluation design

Apply deep security domain expertise to identify, scope, and solve complex security problems that require multi-step reasoning, novel approaches, and expert judgment
Build security products, tools, and capabilities — prototyping AI-driven solutions to real-world security challenges at Meta's scale
Design and execute adversary research, threat analysis, detection engineering, or investigation workflows augmented by AI tools and agents
Identify where AI models lack security reasoning capability and directly contribute to improving them through expert-generated signal, evaluation, and feedback
Develop novel methodologies and reusable approaches that advance both Meta's security posture and AI system performance in security domains
Collaborate with model researchers to translate security expertise into training signal — decomposing hard problems into structured challenges that teach models to reason like security experts
Partner cross-functionally with engineering, product, and research teams to incorporate security innovations into production AI systems
Drive end-to-end execution of complex security initiatives with increasing independence, contributing to technical direction within the team
Disseminate findings through internal publications, knowledge sharing, and contributions to the broader security community