Security Engineer, Applied AI

Meta; City of Westminster; On-site; GBP 100,000 - 150,000; Full time

Job summary

Meta is seeking Security Engineers to join the Applied Artificial Intelligence (AAI) organization. The role blends deep security engineering with applied AI research to build novel AI-powered defenses and scalable AI-native security systems.

Responsibilities include prototyping AI-driven security solutions, adversary research, and translating security expertise into training signals for models, with cross-functional collaboration across engineering, product, and research teams.

Qualifications

• Advanced degree or equivalent experience in security engineering or related field.
• 5+ years in hands-on security engineering across multiple domains.
• Extensive knowledge of attacker TTPs.
• Proficiency in coding with Python, Go, C/C++, or shell scripting.
• Experience building security systems and tooling, not just identifying problems.
• Ability to work independently on complex security challenges.
• Experience integrating AI tools to optimize workflows.
• Experience with responsible, ethical AI practices and evaluation.
• Ongoing AI skill development (prompt engineering, agents) and staying current.

Responsibilities

• Apply deep security expertise to identify and solve complex problems requiring multi-step reasoning.
• Build security products and prototyping AI-driven defenses at scale.
• Design and execute adversary research and threat analysis augmented by AI tools.
• Improve AI models with security signaling, evaluation, and feedback.
• Develop reusable approaches to advance security posture and AI system performance.
• Collaborate with model researchers to create training signals for security experts.
• Partner with engineering, product, and research to productionize security innovations.
• Drive end-to-end execution of complex security initiatives with independence.
• Disseminate findings through internal publications and community contributions.

Skills

Deep security domain expertise

Adversary research

AI tool integration

Security product engineering

Threat intelligence

Cloud security

Incident response

Forensic investigation

Python

Go

C/C++

Shell scripting

AI prompt engineering

Agent orchestration

Distributed systems

Security tooling

Model evaluation

Red-teaming

DLP/IR tooling

Threat modeling

Security research

Production AI systems

Ethical AI practices

Cloud detection and response

Supply chain security

Mobile platform security

Network protocol security

AI model performance optimization

Education

B.S./M.S. in CS, Cybersecurity, or related field

PhD + 2 years security engineering

Tools

Python

Go

C/C++

Shell scripting

Job description

Meta is seeking Security Engineers to join our Applied Artificial Intelligence (AAI) organization. As a Security Engineer in AAI, you will apply deep domain expertise to solve hard, real-world security problems - building novel security capabilities, prototyping AI-powered defenses, and advancing the frontier of what AI systems can do in security. Your work is AI-augmented from day one: you leverage AI tools and agents to accelerate your impact, and where the models fall short, your expertise directly drives their improvement.You will blend hands‑on security engineering with applied research - designing and executing novel approaches to security challenges that push both the state of Meta's security posture and the state of the art in AI-driven security. This includes building security products and tools, conducting original adversary research, developing detection and response capabilities, and translating security expertise into scalable, AI-native systems. Your domain knowledge -
whether in detection engineering, threat intelligence, cloud security, adversary simulation, forensic investigation, or emerging areas - becomes the foundation for capabilities no existing AI system can replicate.

This role is for security practitioners who want to operate at the intersection of deep security expertise and frontier AI - not just using AI as a tool, but shaping what it is capable of.

Required Skills:

Security Engineer, Applied AI Responsibilities:

• 1. Apply deep security domain expertise to identify, scope, and solve complex security problems that require multi-step reasoning, novel approaches, and expert judgment
• 2. Build security products, tools, and capabilities - prototyping AI-driven solutions to real-world security challenges at Meta's scale
• 3. Design and execute adversary research, threat analysis, detection engineering, or investigation workflows augmented by AI tools and agents
• 4. Identify where AI models lack security reasoning capability and directly contribute to improving them through expert-generated signal, evaluation, and feedback
• 5. Develop novel methodologies and reusable approaches that advance both Meta's security posture and AI system performance in security domains
• 6. 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
• 7. Partner cross-functionally with engineering, product, and research teams to incorporate security innovations into production AI systems
• 8. Drive end-to-end execution of complex security initiatives with increasing independence, contributing to technical direction within the team
• 9. Disseminate findings through internal publications, knowledge sharing, and contributions to the broader security community

• 10. 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
• 11. 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
• 12. Extensive knowledge of attacker tactics, techniques, and procedures
• 13. Proficiency in coding with experience in languages such as Python, Go, C/C++, or shell scripting
• 14. Experience building security systems, tools, or capabilities - not just identifying problems but engineering solutions
• 15. Demonstrated ability to operate independently on complex, ambiguous security challenges,
• 16. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• 17. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• 18. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• 19. Experience leveraging AI tools (LLMs, agents, orchestration systems) to accelerate security workflows and enhance operational capability
• 20. Contributions to the security community (original research, tools, CTF design, conference presentations, publications)
• 21. Experience creating structured methodologies that scale security expertise across teams
• 22. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• 23. Experience planning and executing adversary simulation campaigns or purple team exercises at scale
• 24. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• 25. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• 26. Experience with cloud security operations (AWS/GCP/Azure), cloud detection and response, or cloud-native defense
• 27. Experience with forensic investigation - reconstructing attack timelines from evidence across multiple sources
• 28. Background in supply chain security, mobile platform security, network protocol security, or AI/agent security
• 29. Experience improving AI model performance through expert feedback, red-teaming, or evaluation design