Embedded AI Platform Security Engineer

Join our Innovation Team, where we explore cutting-edge concepts at the intersection of Machine Learning and Security. Our mission is to develop forward-looking solutions—such as model protection, privacy-preserving ML, security for agentic AI, and anomaly detection—that will later be integrated into our Edge products. This requires high-level innovation skills combined with a hands-on mindset.

If you are passionate about building secure AI systems, exploring new ideas, and turning concepts into prototypes, this role is for you:

Design and implement security solutions for customer-provided ML models running on embedded hardware using secure execution environments and enclaves.

Responsibilities:

• Develop secure boot and firmware integrity mechanisms for AI-enabled embedded platforms.
• Implement runtime hardening and isolation for ML workloads.
• Enable model protection using Trusted Execution Environments (TEE) and secure enclaves.
• Ensure secure loading and execution of ML models (ONNX, TensorFlow Lite) on edge devices.

Required Skills:

• Have a background in Computer Science, Cybersecurity, or Cryptography and a strong interest in applied ML, OR
• Have a background in Machine Learning and an interest in cybersecurity.
• Embedded programming (C/C++, Rust).
• Understanding of secure boot, firmware integrity, and runtime security.
• Experience with or interest in TEEs, TPM integration, and secure enclaves.
• Familiarity with ML model formats and deployment workflows.
• Knowledge of cryptographic primitives for integrity and confidentiality.

Please note: The successful candidate may/will be responsible for security related tasks. The assignment may/will be in scope of security certifications, therefore a conscious and reliable way of working is necessary.

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