Senior Security Engineer, AI Architecture Patterns

NVIDIA•Us, CA

6d•Onsite

About The Position

NVIDIA is seeking a Senior Security Engineer to build enterprise-grade security architecture patterns for AI tooling. Based in Santa Clara, California, this role focuses on embedding security into AI-generated code and helping advance AI solutions from prototypes to reliable, supportable enterprise products. The engineer will establish architecture standards, develop secure reference implementations, and work with teams building AI and automation solutions to support adoption, validation, and residual-risk closure.

Requirements

At least 8 years of experience 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 tools for building their output, including context and constraint injection, rules and scaffolds, plan mode, tool integrations, and specification-guided development.
Experience building evaluation harnesses and regression suites for non-deterministic systems, including reference cases, result verification, conformance measurement, and regression detection.
Proficiency with cloud-native and containerized environments, including Kubernetes, Helm, container image hardening, and secure composition of identity, networking, and infrastructure primitives.
Strong software engineering fundamentals in a language such as Python or Go.
Experience with CI/CD pipelines, static analysis, code generation tooling, policy-as-code, schema validation, Git workflows, and automated publishing.
Track record taking systems from prototype to production at enterprise standards, including reliability, versioning, observability, access control, documentation, and support models.
Practical security architecture knowledge, including threat modeling, control selection, isolation boundaries, workload identity, secrets handling, logging, and audit, and ability to make specifications machine-checkable.
Ability to work as both an architect and engineer, review builds, explain technical trade-offs, influence without direct authority, and improve team capabilities.
BS/MS in Computer Science, Engineering, or equivalent experience.

Nice To Haves

Experience shipping tooling that steers or constrains code-generating agents at generation time.
Experience providing attestation, provenance, or supply-chain evidence for downstream use, including signing, verifiable claims, or build-time evidence.
Experience productizing internally built AI tooling, taking demos to enterprise-grade solutions with evaluation, guardrails, cost controls, and operational support.
Experience establishing a golden-path, paved-road, or internal developer platform program at scale, including content models, tooling, and adoption strategies.
Ongoing technical development or knowledge sharing through open-source work, technical writing, standards work, conference talks, mentoring, or community participation.

Responsibilities

Develop shift-left solutions that embed security standards and guardrails into AI-generated code during the coding process.
Convert written security standards into machine-enforceable formats for programming assistants and pipelines.
Engineer verification workflows that validate generated code against claimed standards and provide evidence for release gating and policy enforcement.
Maintain and evolve evaluation harnesses, reference repositories, known-bad cases, and encouraged findings for assessing constraint-steered agents.
Turn approved architecture patterns into secure, runnable reference implementations, including templates, Helm charts, container images, and worked examples.
Elevate AI solutions from prototypes to enterprise-grade products, with attention to versioning, observability, latency and cost budgets, access control, audit, graceful failure, support, and deprecation.
Establish enterprise-grade architecture standards and qualification criteria for AI tooling in a security context, and monitor adoption and outcomes.
Collaborate with teams developing AI and automation solutions by reviewing builds, sharing platforms, raising engineering standards, and supporting adoption, validation, and residual-risk closure.