OR PhD + 2 years of hands-on engineering experience 5+ years of hands-on engineering experience in Trust & Safety, integrity, fraud and abuse, or security investigations Demonstrated depth in at least one abuse domain: 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 investigating and acting on high-impact threats and online threat actors Experience coding in at least one of the following or similar languages: PHP, Python, or Java Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience with campaign-level investigation and attribution of coordinated adversary networks Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) 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 Experience with age assurance and age-signal inference, including calibrated confidence and mode switching Contributions to the Trust & Safety or security community (original research, tools, conference presentations, publications) Experience developing and evaluating classifiers for abuse, fraud, or policy enforcement Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience improving AI model performance through expert feedback, red-teaming, or evaluation designMeta builds technologies that help people connect, find communities, and grow businesses. 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.