Privacy Security Engineer, Privacy & Data Protection - Applied AI

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

Minimum 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

Preferred Qualifications

• 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

About Meta

Equal Opportunity

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