Aiml Security Engineering

Apple Apple · Big Tech · Cupertino, CA · Machine Learning and AI

Seeking an experienced AI/ML Security Engineer to design and implement security frameworks for AI/ML pipelines, conduct security assessments, develop automated testing and monitoring, lead incident response, and establish secure MLOps practices. The role also involves strategic business leadership, translating risks, developing roadmaps, and collaborating with cross-functional teams.

What you'd actually do

  1. Design and implement comprehensive security frameworks for AI/ML pipelines, from data ingestion through model deployment
  2. Conduct security assessments of machine learning deployments, identifying vulnerabilities including adversarial attacks, data poisoning, and model inversion risks
  3. Develop automated security testing and monitoring solutions for AI/ML systems at scale
  4. Lead incident response for AI/ML security events, coordinating technical remediation and stakeholder communication
  5. Establish secure MLOps practices, including secure model versioning, access controls, and audit trails

Skills

Required

  • cybersecurity
  • AI/ML security
  • adversarial ML
  • data poisoning
  • model inversion
  • secure cloud architectures
  • containerization technologies (Kubernetes, Docker)
  • Python
  • Swift
  • C++
  • TensorFlow
  • PyTorch
  • Core ML
  • GDPR
  • CCPA
  • AI governance frameworks

Nice to have

  • differential privacy
  • federated learning
  • privacy-preserving ML techniques
  • threat modeling
  • security architecture design
  • accessibility considerations in AI/ML systems

What the JD emphasized

  • 7+ years of experience in cybersecurity with 4+ years specifically in AI/ML security
  • Deep understanding of machine learning security threats (adversarial ML, model stealing, data poisoning, etc.)
  • Experience with regulatory compliance in AI/ML contexts (GDPR, CCPA, AI governance frameworks)

Other signals

  • security frameworks for AI/ML pipelines
  • security assessments of machine learning deployments
  • automated security testing and monitoring solutions for AI/ML systems
  • incident response for AI/ML security events
  • secure MLOps practices