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Apple's Security Engineering & Architecture organization is seeking an Applied Machine Learning Engineer to develop AI-enhanced security analysis systems. You will work within the Security Engineering & Architecture (SEAR) team, which is responsible for securing all Apple products across more than a billion devices.
In this role, you will integrate deeply with security research teams to understand the challenges of analyzing large, complex systems across Apple's full stack—from custom silicon and microarchitectural elements to boot ROMs, firmware, kernels, system frameworks, web browsers, and user applications. You will design and develop AI-enhanced systems using large language models, agentic workflows, and machine learning approaches that complement other analysis methods such as fuzzing, static and dynamic analysis, and manual inspection.
Your work will leverage raw data and expert behavior to create practical, scalable approaches that help researchers navigate vast codebases, reason about intricate attack surfaces, and identify subtle weaknesses that are challenging to detect manually. You will collaborate regularly with security researchers to validate and challenge your innovations during real-world security evaluations, ensuring your work directly impacts meaningful security improvements.
This position provides rare exposure to a full-stack view of security along with direct access to expert knowledge, unique datasets, and cross-domain experience. Your contributions will materially raise the security of products used by billions and strengthen Apple's ability to defend against adversaries.
REQUIREMENTS:
Minimum Qualifications:
- Solid understanding of machine learning algorithms (supervised, unsupervised, and reinforcement learning) and evaluation methods
- Hands-on experience with agentic frameworks such as LangChain, LlamaIndex, or AutoGen to build multi-step, tool-augmented agent workflows
- Familiarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow or similar
- Strong software engineering and programming skills in languages like Python, C, C++, Swift, or Objective-C
- Collaborative and effective problem-solving and analytical skills
Preferred Qualifications:
- Experience scaling LLM training and fine-tuning (pretraining, SFT, alignment)
- Strong enthusiasm for security, especially offensive security
- Familiarity with software-analysis techniques such as fuzzing, static analysis, code-analysis tooling, reverse engineering, and binary-analysis
