Security Engineering Manager, Applied AI

Meta Careers; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Meta is seeking a Security Engineering Manager to lead a team of domain experts in security, privacy, and trust and safety. You will drive health, engagement, growth, and performance while shaping the technical output and expert signal your team delivers across the AI safety domain.

This applied research environment is early-stage, with problem selection led by domain experts and a focus on measurable impact through responsible AI practices and continuous skill development.

Qualifications

• Bachelor's or higher in CS, cybersecurity or related field, or equivalent experience.
• Experience managing security/privacy/ integrity teams and leading expert-driven work.
• Proficiency with Python, Go, C/C++, or shell scripting.

Responsibilities

• Lead a team of security, privacy, and trust and safety domain experts.
• Balance team health and performance with delivery of high-quality expert signal.

Skills

Security engineering

People management

AI tool usage

Python

Go

C/C++

Shell scripting

Adversarial analysis

Education

B.S. or M.S. in Computer Science, Cybersecurity, or related field, or equivalent experience

Tools

AI tooling

Job description

Meta is seeking a Security Engineering Manager to lead a team of domain experts in our Applied Artificial Intelligence (AAI) organization. The team spans security, privacy, and trust and safety, and its mandate is to solve hard, real-world problems in those domains and turn the expert reasoning behind those solutions into the signal that improves what AI systems are capable of. Improved models ship back to the engineering and operations teams who do this work at Meta, so your team's output compounds: it raises the capability of both the models and the practitioners who depend on them.

As a manager here you are accountable for two things at once. The first is your team: health, engagement, growth, and performance for a group of experienced practitioners. The second is the technical output: the volume, quality, and complexity of the expert signal your team produces, prioritized against the capability gaps that matter most. You will also keep a hand in the work itself, because in this environment credibility with your team comes from current domain judgment rather than from past experience.

This is an applied research environment at an early stage. The problems are real and high priority, the road is not fully paved, and problem selection sits with the domain experts rather than being handed down. It is a strong fit for a manager who is energized by that and a poor fit for someone who wants a settled roadmap and a stable problem set.

B.S. or M.S. in Computer Science, Cybersecurity, or related field, or equivalent experience8+ years of experience in security, privacy, integrity, trust and safety, or a related technical field, including hands‑on technical management - 4+ years of experience in people management and organizational leadership. Demonstrated technical depth in at least one of security, privacy, integrity, or trust and safety, sufficient to set direction and assess the quality of expert technical work. Proficiency in coding with experience in languages such as Python, Go, C/C++, or shell scripting. Experience leveraging AI tools to redesign workflows and drive measurable impact, such as efficiency gains or quality improvements. Demonstrated ability to lead teams through complex, ambiguous problems where the roadmap is still being defined. 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 creating structured methodologies that scale domain expertise across teams. Experience improving AI model performance through expert feedback, red-teaming, or evaluation design. Contributions to the security, privacy, or integrity community (original research, tools, conference presentations, publications). Demonstrated depth in adversarial analysis of software systems, abuse and integrity systems, or privacy and data protection engineering. Experience designing benchmarks, evaluation harnesses, or other measurement for technical capability. Experience growing individual contributors to the next level, with the growth attributable to your coaching.