Sr. Software Development Engineer

AMD AMD · Semiconductors · Bellevue, WA +1 · Engineering

This role focuses on researching, designing, and developing security architectures for semiconductor and cryptographic systems, with a specific emphasis on privacy-preserving machine learning using homomorphic encryption for secure AI operations on GPUs. It involves side-channel analysis, strengthening TEE and SEV systems, and designing secure computation frameworks for ML accelerators, contributing to FIPS certification readiness.

What you'd actually do

  1. Research, design, develop, and evaluate security architectures for semiconductor and cryptographic systems
  2. conduct comprehensive side-channel analysis (SCA) on cryptographic hardware and post-quantum cryptography (PQC) implementations to identify and mitigate vulnerabilities
  3. architect and implement privacy-preserving machine learning frameworks utilizing homomorphic encryption for secure AI operations on GPUs
  4. strengthen security architectures for Trusted Execution Environments (TEE) and Secure Encrypted Virtualization systems
  5. design and implement test environments to validate Secure Memory Encryption and cryptographic components for system-on-chip (SoC) platforms, contributing to FIPS certification readiness

Skills

Required

  • Hardware security and cryptographic engineering
  • Side-channel analysis (SCA) and physical attack evaluation
  • Post-quantum cryptography (PQC) implementation and optimization
  • Homomorphic encryption and privacy-preserving machine learning
  • Secure processor architecture design
  • Trusted Execution Environments (TEE) and virtualization security
  • System-on-chip (SoC) security and Root of Trust (RoT) systems
  • Machine learning and artificial intelligence frameworks
  • Python, C/C++, and Verilog
  • Security analysis tools (ChipWhisperer, Riscure Inspector)
  • Cryptographic hardware accelerators and coprocessors
  • Secure Memory Encryption (SME) and Secure Encrypted Virtualization (SEV)
  • FPGA design and hardware description languages
  • Unix/Linux environments and embedded systems

What the JD emphasized

  • security architectures for semiconductor and cryptographic systems
  • side-channel analysis (SCA)
  • post-quantum cryptography (PQC)
  • privacy-preserving machine learning
  • homomorphic encryption
  • Trusted Execution Environments (TEE)
  • Secure Encrypted Virtualization systems
  • FIPS certification readiness
  • secure computation frameworks for machine learning accelerators

Other signals

  • privacy-preserving machine learning
  • homomorphic encryption
  • secure AI operations on GPUs
  • security architectures for Trusted Execution Environments (TEE)
  • Secure Encrypted Virtualization systems
  • FIPS certification readiness
  • secure computation frameworks for machine learning accelerators