Privacy Engineering Cybersecurity Jobs

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Privacy Security Engineer, Privacy & Data Protection - Applied AI

Meta Seattle, WA

Seattle, WA

Today

$184k - $257k/yr Full time Ai Security Applied Ai Engineer Artificial Intelligence

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Job Description

Meta’s Applied AI organization is hiring a Privacy Security Engineer (Privacy & Data Protection) to bring privacy engineering expertise to practical, real-world AI data lifecycle and privacy obligations challenges. This role focuses on building privacy capabilities and evaluation infrastructure, setting durable standards across teams, and partnering across the org to define how privacy and AI intersect at Meta scale.

What you’ll do

Work through privacy and data protection challenges where the solution and requirements are initially unclear, delivering outcomes that span multiple teams
Architect privacy capabilities and evaluation infrastructure that establish the bar across teams
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 how privacy expertise translates into AI capabilities, including methodologies that make model improvement systematic and scalable
Build cross-organizational partnerships that influence technical direction beyond the immediate team
Break ambiguous efforts into scoped work streams, owning the most technically complex portions while enabling others to execute
Mentor engineers, help build a community, and model privacy engineering practices and Meta values
Share results through internal publications, knowledge sharing, and contributions to the broader privacy and security community

What you bring

B.S. or M.S. in Computer Science, Cybersecurity, or a related field, or equivalent experience
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, such as 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, not only reviewing or advising
Strong software engineering skills, including code review and maintenance; experience coding in Python, PHP, Java, C/C++, or an equivalent language
Proven ability to architect privacy systems, controls, or programs that operate at scale and guide how others build
Demonstrated ability to define and drive complex, ambiguous problem spaces with group-level impact

Technologies

Python
PHP
Java
C/C++

Compensation

USD 184,000 to 257,000 per year + bonus + equity + benefits.

Benefits

bonus
equity
benefits

Preferred qualifications

Experience adhering to and implementing responsible, ethical AI practices (risk assessment, bias mitigation, 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 with measurable impact (efficiency gains, quality improvements)
Ongoing AI skill development and staying current with emerging AI technologies (prompt/context engineering, agent orchestration)
Experience implementing obligations conditioned on jurisdiction, age band, or cohort, such as regional carve-outs and regulatory deadlines, including minor and parent-initiated request handling
Contributions to the privacy or security community (original research, tools, conference presentations, publications)
Experience applying differential privacy, k-anonymity, or other formal de-identification techniques in production environments rather than research only
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

Location: Seattle, WA (onsite)

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