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

Apply deep privacy and data protection expertise to identify, scope, and solve complex problems that require multi-step reasoning, novel approaches, and expert judgment Build privacy capabilities, tools, and evaluation environments, prototyping 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 where AI models lack privacy reasoning capability and directly contribute to improving them through expert-generated signals, evaluation, and feedback Collaborate with model researchers to translate privacy expertise into training signals, decomposing hard problems into 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