Security Engineer, Trust & Safety - Applied AI

Meta is seeking Security Engineers with Trust & Safety expertise to join our Applied Artificial Intelligence (AAI) organization. You will apply deep domain expertise in online abuse and adversarial behavior, across areas such as child safety, human exploitation, fraud and scams, violent extremism, and coordinated inauthentic behavior, to advance what AI systems can do in Trust & Safety. 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.

The problems here are the ones where abuse comes from behavior, context, and scale rather than a software defect: a model that overtrusts an asserted authorization, an agent pressured into a policy exception, a campaign in which every artifact looks benign on its own. Models recognize public benchmarks far more readily than internal ones, so building evaluations that hold up is part of the engineering process.

This role is for Trust & Safety practitioners who want to operate at the intersection of deep domain expertise and frontier AI, not just applying AI to protect from and prevent adversarial behavior, but shaping what it is capable of.

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

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 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