Cloud Security Architecture Lead

NVIDIA NVIDIA · Semiconductors · CA +2 · Remote

Lead the security architecture for global cloud and datacenter infrastructure, focusing on protecting AI workloads against advanced adversaries. Responsibilities include architecting security frameworks for public and private GPU/AI environments, implementing network segmentation, facilitating confidential computing, crafting encryption schemes, governing workload identity, and directing security in container/Kubernetes environments. Requires extensive experience in security engineering/architecture, cloud-native security, workload identity, and container security.

What you'd actually do

  1. Architecting robust security frameworks for cloud infrastructure across public (AWS, Azure, GCP, Oracle) and private GPU/AI environments.
  2. Implementing network segmentation strategies using SDN techniques to implement multi-tenant isolation.
  3. Facilitating adoption of Confidential Computing and hardware-rooted trust models for high-sensitivity AI workloads.
  4. Crafting layered encryption schemes to mitigate risks and adhere to Zero Trust principles.
  5. Governing workload identity frameworks to eliminate static credentials and implement identity-based access controls.

Skills

Required

  • 15+ years in security engineering or architecture roles
  • 8 years in senior leadership positions
  • architecting security for both "Security of the Cloud" and "Security in the Cloud."
  • cloud-native security architectures on major cloud platforms
  • workload identity and zero-trust access patterns
  • protecting containerized and Kubernetes environments on a large scale
  • BS/MS or PhD in Computer Science, Electrical Engineering, or related field, or equivalent experience

Nice to have

  • Experience securing AI/ML infrastructure and GPU compute clusters against novel threats
  • Experience with hardware-rooted trust and secure provisioning pipelines
  • Depth in post-quantum cryptography and applied cryptography in distributed systems

What the JD emphasized

  • protecting AI workloads
  • AI/ML infrastructure
  • GPU compute clusters