Velaura AI
Security Architect
$200K–$300K
Santa Clara, CA · On-site · Full-time · Posted Jul 27, 2026
- Experience
- 10+ years in security engineering or architecture.
- Education
- Master’s degree in Electrical/Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
About the job
Own the end-to-end security architecture for Physical AI SoCs, from early design through production, protecting AI models, sensor data, and safety-critical functions.
What you'll do
- Define SoC security architecture, including root of trust, secure boot, key management, secure debug, and lifecycle provisioning.
- Lead threat modeling and translate security risks into hardware and firmware requirements across silicon, firmware, safety, and platform teams.
- Set security standards and review practices, and support secure updates, attestation, certification, audits, and customer assurance.
What they ask for
- 10+ years in security engineering or architecture, including significant hardware/SoC security experience for shipping silicon or embedded platforms.
- Expertise in hardware security primitives and applied cryptography, including root of trust, secure boot, TEEs, key management, secure debug, and physical and side-channel attack countermeasures.
- Hands-on SoC development experience spanning RTL/silicon design, firmware, and low-level platform software.
- Proven ability to lead threat modeling and drive security requirements across multidisciplinary teams; Master’s degree in a related field or equivalent practical experience.
Nice to have
- Experience securing AI/ML accelerators or NPUs; familiarity with robotics, autonomous systems, or safety-critical domains and related standards.
- Experience with post-quantum cryptography, confidential computing, hardware supply-chain security, or building security practices at a semiconductor or hardware company.
Pay
USD 200,000–300,000 (per-year-salary) — base salary
Tools
SoC, RTL, TrustZone, NPU, ISO 26262, ISO/SAE 21434, Common Criteria, FIPS 140-3
AI focus
Protect AI models and workloads on-chip, including model confidentiality and integrity, secure AI/NPU execution, memory isolation, trusted execution environments, and cryptographic acceleration.
Model-written summary · the original posting is authoritative