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 models, tooling, and adoption strategies.
Engagement in ongoing technical development or knowledge sharing to broaden risk reduction strategies, through open-source work, technical writing, standards work, conference talks, mentoring, or community participation.
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 168,000 USD – 270,250 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
