Job Description

Meta is seeking Security Engineers with Privacy and Data protection expertise to join our Applied Artificial Intelligence (AAI) organization. You will apply deep privacy engineering expertise to hard, real-world problems in how systems collect, use, retain, expose, and delete data. 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 that matter here are the ones a capable engineer cannot solve by reading the code in front of them: which jurisdiction's deadline applies to a deletion request, whether a derived dataset clears an anonymity threshold, where a deletion has to cascade. Models already handle the privacy obligations that are stated as ordinary good engineering practice, so the work is the obligation they cannot derive.

This role is for privacy engineers who want to operate at the intersection of deep privacy expertise and frontier AI, not just applying AI to privacy work but shaping what it is capable of.

Responsibilities

Apply deep privacy and data protection expertise to identify, scope, and solve complex problems that require multi-step reasoning, novel approaches, and expert judgment

• Build privacy capabilities, tools, and evaluation environments, prototyping AI-driven solutions to real-world privacy problems at Meta's scale
• Design and execute privacy obligation analysis, de-identification assessment, data lifecycle review, or data access reasoning augmented by AI tools and agents
• Identify where AI models lack privacy reasoning capability and directly contribute to improving them through expert-generated signals, evaluation, and feedback
• Collaborate with model researchers to translate privacy expertise into training signals, decomposing hard problems into structured challenges that teach models to reason like privacy engineers
• Partner with legal, policy, product, and infrastructure teams to translate obligations into engineering requirements
• Own complex privacy initiatives end to end, from problem definition through delivery, and contribute to technical direction within the team
• Disseminate findings through internal publications, knowledge sharing, and contributions to the broader privacy and security community

Qualifications

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

• 5+ years of hands-on engineering experience in technical privacy, data protection, or privacy-adjacent security engineering
• Deep expertise in at least one privacy subdomain with demonstrated ability to advance the state of the art: purpose limitation, retention and deletion, data access control, jurisdictional obligations, de-identification, consent, or ML and model privacy
• Experience implementing privacy obligations in production systems, in addition to reviewing or advising on them
• Software engineering proficiency, including code review and maintenance, with experience coding in Python, PHP, Java, C/C++, or an equivalent language
• Track record of architecting privacy systems, controls 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 Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience applying differential privacy, k-anonymity, or other formal de-identification techniques in production rather than in research only
• Experience with pseudonymization lifecycle, including deletion of the user-to-pseudonym mapping
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience implementing obligations conditioned on jurisdiction, age band, or cohort: regional carve-outs, regulatory deadlines, minor and parent-initiated request handling
• Experience improving AI model performance through expert feedback, red teaming, or evaluation design
• Background in ML and model privacy: memorization, membership inference, training-data extractability, disclosure through model outputs
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Contributions to the privacy or security community (original research, tools, conference presentations, publications)
• Experience with privacy review or privacy incident response at scale