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Applied Machine Learning Engineer - Security

Apple -
Paris (75)

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Détails de l'emploi

Qualifications

Rust
Systèmes d'exploitation
Objective-C
C++
C
Securité
Swift
Python

Description complète du poste

Apple's Security Engineering & Architecture organization is responsible for the security of all Apple products. Passionate about safeguarding users, we believe that the best defense requires a great offense. When it comes to securing more than a billion devices running the world's most sophisticated operating systems, that means finding vulnerabilities first.

Our mission is to discover, understand, and exploit vulnerabilities across all layers of Apple’s platforms, and we believe that ML techniques significantly enhance our ability to do so. We are seeking an Applied Machine Learning Engineer who will help us invent and deliver these new methods and techniques.

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.

Can you make a difference on this scale? Join our extraordinary group of security researchers, tool developers, and machine learning experts, and help protect all Apple users.

Description
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 browser and user applications.

You will design and develop ML-enhanced systems - using large language models, generative modeling, agentic workflows and other approaches - that complement other analysis methods such as fuzzing, static & dynamic analysis, and manual inspection. Your work will leverage raw data and expert behavior to create practical, scalable approaches to help researchers navigate vast codebases, reason about intricate attack surfaces, and identify subtle weaknesses that are challenging to detect manually. You will also collaborate regularly with security researchers to validate and challenge your innovations during real-world security evaluations, ensuring that your work will directly affect meaningful impact.

Preferred Qualifications
Familiarity with software-analysis techniques such as fuzzing, static analysis, code-analysis tooling, reverse engineering, binary-analysis

Familiarity with security mitigations in modern operating systems

Minimum Qualifications
Expertise in ML, especially large language models and generative modeling

Experience with and/or strong enthusiasm for security, especially offensive security

Fluency with software engineering using languages such as C, C++, Python, Swift, Objective-C, Rust

Collaborative and effective problem-solving and analytical skills

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