Privacy Security Engineer, Privacy & Data Protection - Applied AI

Meta•Menlo Park, CA

1d•$184,000 - $257,000

About The Position

Meta is seeking a Privacy Security Engineer to join its Applied Artificial Intelligence organization, applying deep privacy engineering expertise to real-world challenges involving how systems collect, use, retain, expose, and delete data. The role combines privacy expertise with AI: using AI tools and agents to accelerate work, and applying expert knowledge to improve model capabilities where they fall short. The position focuses on privacy problems that require expert reasoning beyond ordinary engineering practice, such as determining applicable jurisdictional deadlines for deletion requests, assessing whether derived datasets meet anonymity thresholds, and identifying where deletion must cascade. The engineer will develop privacy capabilities and evaluation infrastructure, set approaches for privacy problems AI cannot yet reason about, and help shape how privacy expertise is translated into scalable AI capabilities. The role also involves cross-organizational technical influence, mentoring, knowledge sharing, and contributions to the privacy and security community.

Requirements

B.S. or M.S. in Computer Science, Cybersecurity, or a related field, or equivalent experience.
At least 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, beyond reviewing or advising on them.
Software engineering proficiency, including code review and maintenance, and 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.

Nice To Haves

Experience adhering to and implementing responsible, ethical AI practices, such as risk assessment, bias mitigation, and 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.
Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Ongoing AI skill development, such as prompt or context engineering and agent orchestration, and keeping current with emerging AI technologies.
Experience implementing obligations conditioned on jurisdiction, age band, or cohort, including regional carve-outs, regulatory deadlines, and minor- and parent-initiated request handling.
Contributions to the privacy or security community, such as original research, tools, conference presentations, or publications.
Experience applying differential privacy, k-anonymity, or other formal de-identification techniques in production rather than only in research.
Experience with privacy review or privacy incident response at scale.
Background in ML and model privacy, including memorization, membership inference, training-data extractability, and disclosure through model outputs.

Responsibilities

Identify and solve ambiguous privacy and data protection problems with initially unknown solutions and requirements, driving solutions that affect multiple teams.
Architect privacy capabilities and evaluation infrastructure that set standards across teams, and drive platform-level decisions for AI-driven privacy solutions at Meta's scale.
Lead strategy and methodology for privacy problems AI cannot yet reason about, including jurisdictional and regulatory obligations, de-identification standards, data lifecycle controls, and data access reasoning.
Define methodologies that make improvement of AI capabilities through privacy expertise systematic and scalable.
Drive cross-organizational partnerships that shape the intersection of privacy and AI, influencing technical direction beyond the immediate team.
Break down problems into scoped work streams, own the most technically complex pieces, and enable others to execute the overall solution.
Mentor engineers across the organization, build a community, and model privacy engineering practices and Meta values.
Share findings through internal publications and knowledge sharing, and contribute to the broader privacy and security community.