Security Engineering Manager, Applied AI

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:

Security Engineering Manager, Applied AI Responsibilities:

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

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

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