Privacy Security Engineer, Privacy & Data Protection - Applied AI

Meta; United States; Remote; USD 180,000 - 320,000; Full time

Job summary

Meta is seeking Security Engineers with Privacy and Data protection expertise to join our Applied AI organization. You will apply deep privacy engineering know‑how to hard, real‑world problems about how systems collect, use, retain, expose, and delete data, with AI augmentation from day one.

This role targets privacy engineers at the intersection of deep privacy and frontier AI, shaping what privacy work can become in production at scale.

Qualifications

• Bachelor’s or Master’s in CS/Cybersecurity or related field, or equivalent experience.
• Deep expertise in at least one privacy subdomain with ability to advance the state of the art.
• Experience implementing privacy obligations in production systems.
• Software proficiency in Python, PHP, Java, C/C++, or equivalent language.
• Track record of architecting privacy systems, controls or programs at scale.
• 5+ years of hands‑on engineering in technical privacy or privacy‑adjacent security engineering.
• Ongoing AI skill development (prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
• Experience applying differential privacy, k‑anonymity, or other de‑identification techniques in production.
• Experience with pseudonymization lifecycle, deletion of user‑to‑pseudonym mapping.
• Experience adhering to responsible, ethical AI practices (risk assessment, bias mitigation).
• Experience implementing jurisdictional obligations, regional carve‑outs, or data‑deletion deadlines.
• Experience improving AI model performance through evaluation and feedback.
• Background in ML and model privacy (memorization, membership inference, etc.).
• Ability to integrate AI tools to optimize workflows and drive measurable impact.
• Contributions to privacy or security community (original research, tools, talks).
• Experience with privacy review or privacy incident response at scale.

Skills

Privacy engineering

Production privacy obligations

Programming languages

Privacy systems architecting

Senior engineering experience

AI/ML privacy skills

De-identification techniques

Pseudonymization lifecycle

Responsible AI practices

Jurisdictional data handling

AI tools and orchestration

Privacy community contributions

Privacy incident response

Model privacy concepts

Integrating AI to improve workflows

Education

B.S./M.S. in Computer Science 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
• 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
• 5+ years of hands‑on engineering experience in technical privacy, data protection, or privacy‑adjacent security engineering
• 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