machine learning engineer in security

• Design, build, and deploy production-grade ML models to detect and mitigate abuse across text, image, and audio
• Own the full ML lifecycle from prototyping and data analysis through deployment, monitoring, and continuous production improvement
• Drive data strategy by analyzing distribution gaps, contributing to synthetic data pipelines, and creating automated annotation systems
• Architect end-to-end systems for monitoring platform activity, detecting misuse, and triggering real-time automated enforcement actions
• Collaborate with engineering, research, and product partners to define project requirements, establish technical direction, and deliver robust security solutions

• Proven experience shipping machine learning models to production and owning their end-to-end lifecycle from development through deployment and maintenance
• Strong familiarity with research fundamentals, machine learning principles, and development methodologies for LLMs, foundation models, and diffusion models
• Proficient programming skills in Python and deep learning toolkits such as JAX, PyTorch, or TensorFlow
• Ability to work with sensitive and offensive content when building security and abuse detection systems
• Nice to have: BS, MS, or PhD in Computer Science, Machine Learning, or a related field or equivalent qualification, experience fine-tuning or aligning large language models for security or safety applications, experience building large-scale data processing pipelines and ML infrastructure, experience driving technical projects and collaborating with large, diverse, cross-functional teams

Apple builds large-scale machine learning systems that protect millions of users from emerging threats and ensure the integrity of its products. The Data and ML Innovation team develops security and abuse detection systems while upholding user privacy.