Senior Security Engineer, AI Architecture Patterns

Join NVIDIA, a company with over 25 years of experience advancing visual technology, personal gaming platforms, and high-performance computing! We have pushed innovation through our innovative technology and talented team. As we expand into AI’s vast possibilities, we need a Senior Security Engineer, Architecture Patterns to build the future of computing. In this role, you will be involved in the latest AI developments, uphold security standards, and contribute to powerful projects alongside top talent in Santa Clara, CA. Join us!

What you’ll be doing:

• Develop shift-left solutions that embed security standards directly into AI-generated code, ensuring constraints, scaffolds, rules, and guardrails are applied during the coding process rather than post-shipment.
• Convert written security standards into machine-enforceable formats, enabling programming assistants and pipelines to apply them seamlessly without manual interpretation.
• Engineer verification workflows that confirm generated code meets claimed standards, providing evidence for downstream release gating and policy enforcement.
• Maintain and evolve evaluation harnesses, reference repositories, known-bad cases, and encouraged findings to ensure constraint-steered agents produce conformant code amid changing models, patterns, and tooling.
• Convert approved architecture patterns into secure, runnable reference implementations, such as templates, Helm charts, container images, and worked examples, ensuring their reliability as platforms evolve.
• Elevate AI solutions from prototypes to enterprise-grade products, focusing on versioning, observability, latency and cost budgets, access control, audit, graceful failure, and a detailed support and deprecation model.
• Set the enterprise-grade architecture standards for AI tooling in a security context, determining qualification criteria and monitoring adoption and outcomes to ensure efficiency.
• Collaborate with teams developing their own AI and automation solutions, reviewing builds, sharing platforms, raising engineering standards, and staying engaged through adoption, validation, and residual-risk closure.

What we need to see:

• 8+ years in security engineering, platform engineering, product security, infrastructure security, DevSecOps, developer experience engineering, or software engineering for large distributed systems.
• Expertise with AI coding agents and the tooling that builds their output, including context and constraint injection, rules and scaffolds, plan-mode and tool integrations, and development guided by detailed specifications.
• Proven skill in building evaluation harnesses and regression suites for non-deterministic systems, including reference cases, result verification, conformance measurement, and regression detection.
• Proficiency in cloud-native and containerized environments, including Kubernetes, Helm, container image hardening, and the composition of identity, networking, and infrastructure primitives into secure systems.
• Strong software engineering fundamentals in a language such as Python or Go, with experience in CI/CD pipelines, static analysis, code generation tooling, policy-as-code, schema validation, Git workflows, and automated publishing.
• Proven track record of taking systems from prototype to production at enterprise standards, including reliability, versioning, observability, access control, documentation, and support models.
• Practical grounding in security architecture, including threat modeling, control selection, isolation boundaries, workload identity, secrets handling, logging, and audit, with the ability to render specifications machine-checkable.
• Ability to operate as both an architect and engineer, reviewing builds, explaining technical trade-offs, influencing without direct authority, and improving team capabilities.
• BS/MS in Computer Science, Engineering, or equivalent experience.

Ways to stand out from the crowd:

• Experience in shipping tooling that steers or constrains code-generating agents at generation time.
• Providing attestation, provenance, or supply chain evidence for downstream use, including signing, verifiable claims, or build-time evidence.
• Productizing internally built AI tooling, taking demos to enterprise-grade solutions with evaluation, guardrails, cost controls, and operational support.
• Establishing a golden-path, paved-road, or internal developer platform program at scale, including content mo…