Privacy Security Engineer, Privacy & Data Protection - Applied AI

Meta; Menlo Park (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Meta in Menlo Park seeks Security Engineers with privacy and data protection expertise to join our Applied AI organization. You will apply deep privacy engineering to hard, real-world problems in how data is collected, used, retained, exposed, and deleted.

Your work is AI-augmented from day one, leveraging AI tools to accelerate impact and improve model privacy. This role focuses on privacy at scale, production systems, and collaboration across teams to implement obligations tied to

Qualifications

• 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.

Responsibilities

• Design privacy systems and controls at scale across production environments.
• Collaborate with Applied AI and data teams to ensure privacy obligations are implemented in real-world data workflows.
• Lead efforts on de-identification, consent, data retention, deletion cascades, and jurisdictional requirements.

Skills

Privacy engineering

Production privacy implementation

Python/Java/C/C++

AI tools integration

Ethical AI practices

Data protection

Problem solving at scale

Model privacy

De-identification techniques

Education

BS/MS in CS or Cybersecurity

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.

• 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
• 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