Privacy Security Engineer, Privacy & Data Protection - Applied AI
Responsibilities
Identify and solve ambiguous problems within the privacy and data protection space where the solution and requirements are initially unknown, driving solutions that affect multiple teams
• Architect privacy capabilities and evaluation infrastructure that sets the standard across teams; drive platform-level decisions for AI-driven privacy solutions at Meta's scale
• Lead the strategy and methodology for the privacy problems AI cannot yet reason about: jurisdictional and regulatory obligations, de-identification standards, data lifecycle controls, and data access reasoning
• Shape how privacy expertise translates into AI capabilities: define the methodologies that make model improvement systematic and scalable
• Drive cross-organizational partnerships that define how privacy and AI intersect: influence technical direction beyond the immediate team
• Break down problems into smaller scoped work streams, owning the most technically complex pieces while enabling others to execute against the overall solution
• Mentor other engineers across the organization, build a community, and act as a role model for privacy engineering practices and Meta values
• 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
• 8+ 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
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience improving AI model performance through expert feedback, red teaming, or evaluation design
• Experience with pseudonymization lifecycle, including deletion of the user-to-pseudonym mapping
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience implementing obligations conditioned on jurisdiction, age band, or cohort: regional carve-outs, regulatory deadlines, minor and parent-initiated request handling
• Contributions to the privacy or security community (original research, tools, conference presentations, publications)
• Experience applying differential privacy, k-anonymity, or other formal de-identification techniques in production rather than in research only
• Experience with privacy review or privacy incident response at scale
• Background in ML and model privacy: memorization, membership inference, training-data extractability, disclosure through model outputs
