Experteer Overview

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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, xugodme 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

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📌 Applied Machine Learning Engineer - SEAR (Barcelona)
🏢 Apple
📍 Barcelona