Privacy Security Engineer, Privacy & Data Protection - Applied AI

About the job

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.

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