Experteer Overview In this role you will apply machine learning to fortify Apple's security across its full stack, from silicon to user-facing software.
You will build AI-enhanced systems, including large language model workflows, to help researchers navigate massive codebases and identify subtle vulnerabilities.
You'll collaborate with security researchers to validate innovations during real-world evaluations, delivering measurable security impact at scale.
This is a rare opportunity to combine ML with offensive security to protect billions of users.
Compensaciones / Incentivos
• Collaborate with security research teams to understand challenges in analyzing large-scale systems
• Design and develop AI-enhanced systems using LLMs and agentic workflows
• Implement tool-augmented, multi-step workflows to assist vulnerability discovery
• Leverage raw data and expert behavior to build scalable approaches for navigating codebases
• Collaborate with researchers to validate innovations during security evaluations




• Advance ML approaches that complement fuzzing, static/dynamic analysis, and manual inspection Responsabilidades
• Experience scaling LLM training and fine-tuning (pretraining, SFT, alignment)
• Strong enthusiasm for security, especially offensive security
• Familiarity with software-analysis techniques (fuzzing, static analysis, code-analysis tooling, reverse engineering, binary-analysis)
• Solid understanding of ML algorithms (supervised, unsupervised, reinforcement learning) and evaluation methods
• Hands-on experience with agentic frameworks (LangChain, LlamaIndex, AutoGen) to build multi-step, tool-augmented workflows
• Familiarity with MLOps practices (model versioning, CI/CD, experiment tracking tools like MLflow)
• Strong software engineering and programming skills in Python, C, C++, Swift, Objective-C
• Collaborative and effective problem-solving and analytical skills Requisitos principales •

📌 Applied Machine Learning Engineer (España)
🏢 Apple
📍 España