Security Engineer, Trust & Safety - Applied AI

Security Engineer, Trust & Safety - Applied AI Responsibilities Identify and solve ambiguous problems within a Trust & Safety domain where the solution and requirements are initially unknown, driving solutions that affect multiple teams • Architect Trust & Safety capabilities and evaluation infrastructure that set the standard across teams; drive platform-level decisions for AI-driven solutions at Meta's scale • Lead the strategy and methodology for the abuse problems AI cannot yet reason about: age-aware policy boundaries, adversaries who adapt to enforcement, abuse at campaign scale, and policy interpretation under pressure • Shape how Trust & Safety expertise translates into AI capability: define the methodologies that make model improvement systematic and scalable • Drive cross-organizational partnerships that define how Trust & Safety and AI intersect: influence technical direction beyond the immediate team • Break down problems into smaller scoped workstreams, owning the most technically complex pieces while enabling others to execute against the overall solution • Mentor other engineers across the organization, build community, and act as a role model for Trust & Safety engineering practices and Meta values • Disseminate findings through internal publications, knowledge sharing, and contributions to the broader community Minimum Qualifications • B.S. or M.S. in Computer Science, Cybersecurity, or related field, or equivalent experience • 8+ years of hands-on engineering experience in Trust & Safety, integrity, fraud and abuse, or security investigations • Deep expertise in at least one abuse domain with demonstrated ability to advance the state of the art: child safety and youth protection, dangerous organizations and violent extremism, fraud and scams, coordinated inauthentic behavior and information operations, or content policy enforcement • Experience building detection, enforcement or evaluation systems in an abuse domain, in addition to operating or investigating within one • Experience analyzing and interpreting large scale data, including relational databases and SQL, to identify patterns of adversarial behavior and quantify trends • Experience coding in at least one of the following or similar languages: PHP, Python, or Java • Track record of architecting systems or programs that operate at scale and influence how others build • Demonstrated ability to define and drive complex, ambiguous problem spaces with group-level impact Preferred Qualifications • Experience adversarially testing conversational or agentic systems: jailbreaks, social engineering, prompt injection, policy-exception pressure • Experience authoring or interpreting enforcement guidelines at scale, including coded terminology and evolving slang • Contributions to the Trust & Safety or security community (original research, tools, conference presentations, publications) • Experience with campaign-level investigation and attribution of coordinated adversary networks • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) • Experience developing and evaluating classifiers for abuse, fraud, or policy enforcement • Experience improving AI model performance through expert feedback, red-teaming, or evaluation design • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) • Experience with age assurance and age-signal inference, including calibrated confidence and mode switching