Security Engineering Manager, Applied AI

Meta; City Of London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Meta is seeking a Security Engineering Manager in London to lead a team of security, privacy, integrity, and trust experts within the Applied AI organization. The role blends people leadership with hands-on technical work to deliver high-value signals that improve AI models and systems.

You will work with model researchers and partner engineering managers to ensure impactful, credible work while growing team members and shaping the security roadmap.

Qualifications

• Bachelor or Master degree in CS, cybersecurity, or related field, or equivalent experience.
• 8+ years in security, privacy, integrity, or trust and safety with hands-on management.
• 4+ years of people management and organizational leadership.
• Experience coding with Python/Go/C/C++ or similar.

Responsibilities

• Lead a team of engineers across security, privacy, integrity, and trust and safety, focusing on health, engagement, and retention.
• Own people development through regular 1:1s, growth planning, feedback, and performance management.
• Drive the volume, quality, and complexity of the team’s expert signal and align with model performance goals.
• Collaborate with engineering managers, leads, and researchers to ensure workstreams succeed.
• Provide technical direction while remaining an active contributor to maintain domain credibility.
• Initiate incubated research projects and advise on roadmaps and technical assumptions.
• Contribute to recruiting, organizational health, and company-wide engineering programs.

Skills

Security leadership

People management

Python

Go

C/C++

Shell scripting

AI tooling

Risk assessment

Education

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

Tools

Shell scripting

Job description

Summary:

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.

Required Skills:

1. Support, develop, and grow a team of experienced engineers across security, privacy, integrity, and trust and safety, with accountability for team health, engagement, and retention
2. Own people development end to end: regular 1:1s, growth planning, feedback, performance management, and supporting people through team and organizational change
3. Hold accountability for the volume, quality, and complexity of the expert technical signal your team produces, prioritizing against identified capability gaps in model performance
4. Connect your team to the highest-impact work by mapping individual strengths, growth areas, and interests to the right workstreams and projects
5. Provide technical direction and guidance within the domain, and debug issues in quality or output together with your technical leads
6. Maintain direct technical contribution alongside your team so your domain judgment stays current and credible
7. Initiate or advise incubated research projects and internal advisory engagements, including identifying gaps in technical assumptions and roadmaps
8. Collaborate with partner engineering managers, technical leads, and model researchers to ensure workstreams operate successfully
9. Grow individual contributors toward the next level through coaching and by identifying or creating the opportunities that make growth possible
10. Contribute beyond your immediate team to recruiting, organizational health, internal technical forums, and company-wide engineering programs

Minimum Qualifications:

Minimum Qualifications:

1. B.S. or M.S. in Computer Science, Cybersecurity, or related field, or equivalent experience
2. 8+ years of experience in security, privacy, integrity, trust and safety, or a related technical field, including hands‑on technical management
3. 4+ years of experience in people management and organizational leadership
4. 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
5. Proficiency in coding with experience in languages such as Python, Go, C/C++, or shell scripting
6. Experience leveraging AI tools to redesign workflows and drive measurable impact, such as efficiency gains or quality improvements
7. Demonstrated ability to lead teams through complex, ambiguous problems where the roadmap is still being defined

Preferred Qualifications:

Preferred Qualifications:

1. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
2. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
3. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
4. Experience creating structured methodologies that scale domain expertise across teams
5. Experience improving AI model performance through expert feedback, red‑teaming, or evaluation design
6. Contributions to the security, privacy, or integrity community (original research, tools, conference presentations, publications)
7. Demonstrated depth in adversarial analysis of software systems, abuse and integrity systems, or privacy and data protection engineering
8. Experience designing benchmarks, evaluation harnesses, or other measurement for technical capability
9. Experience growing individual contributors to the next level, with the growth attributable to your coaching

Industry: Internet