Privacy Security Engineer, Privacy & Data Protection - Applied AI

Meta•Washington, DC

1d•$154,000 - $217,000

About The Position

Meta is seeking a Privacy Security Engineer with privacy and data protection expertise to join its Applied Artificial Intelligence (AAI) organization. The role applies deep privacy engineering expertise to complex, real-world problems involving how systems collect, use, retain, expose, and delete data. The work uses AI tools and agents to accelerate privacy engineering and contributes expert judgment and feedback to improve AI capabilities where models fall short. Focus areas include interpreting applicable obligations, assessing derived datasets for anonymity, and determining how data deletion should cascade. The engineer will build privacy capabilities, tools, and evaluation environments; prototype AI-driven solutions; and collaborate with model researchers and cross-functional partners. The role owns complex privacy initiatives from problem definition through delivery and contributes to technical direction, internal knowledge sharing, and the wider privacy and security community.

Requirements

B.S. or M.S. in Computer Science, Cybersecurity, or a related field, or equivalent experience.
Deep expertise in at least one privacy subdomain, with demonstrated ability to advance the state of the art. Listed subdomains include purpose limitation, retention and deletion, data access control, jurisdictional obligations, de-identification, consent, and 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.
At least 5 years of hands-on engineering experience in technical privacy, data protection, or privacy-adjacent security engineering.

Nice To Haves

Demonstrated ongoing AI skill development, such as prompt/context engineering or 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 only in research.
Experience with pseudonymization lifecycle, including deletion of the user-to-pseudonym mapping.
Experience adhering to and implementing responsible, ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Experience implementing obligations conditioned on jurisdiction, age band, or cohort, including regional carve-outs, regulatory deadlines, and 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, including memorization, membership inference, training-data extractability, and disclosure through model outputs.
Demonstrated ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Contributions to the privacy or security community, such as original research, tools, conference presentations, or publications.
Experience with privacy review or privacy incident response at scale.

Responsibilities

Apply deep privacy and data protection expertise to identify, scope, and solve complex problems requiring multi-step reasoning, novel approaches, and expert judgment.
Build privacy capabilities, tools, and evaluation environments, and prototype 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 gaps in AI models' privacy reasoning and contribute to improving them through expert-generated signals, evaluation, and feedback.
Collaborate with model researchers to translate privacy expertise into training signals and 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.