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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer - Enterprise AI... - **Company:** NVIDIA Ltd. - **Location:** Santa Clara, CA, United States - **Salary:** $272,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Audit Trail, Cloud Computing, Continuous Integration, Python (Programming Language), Key Management, Linux Security Modules, Open Source Technology, Systems Development Life Cycle, Role-Based Access Control, Regression Testing, Delivery Pipeline, Large Language Models, Generative AI, Kubernetes, Information Technology, Machine Learning Operations - **Published:** May 19, 2026 - **Apply:** https://www.juju.com/job/00000000g0oopx ## About the Role + Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience). + 15+ years of industry experience building and securing large-scale systems, platforms, or infrastructure. + Proven ability to lead complex technical initiatives as a senior IC-setting direction, driving alignment, and delivering outcomes. + Strong understanding of security fundamentals : threat modeling, authentication/authorization, least privilege, secrets management, secure SDLC, and incident response. + Demonstrated experience with sandboxing / isolation technologies (containers, microVMs, Linux security primitives, policy enforcement, runtime controls). + Experience designing systems with strong observability and auditability (structured logs, traceability, metrics, security telemetry). + Familiarity with evaluation and benchmarking approaches for AI/ML systems, including designing tests, measuring behavioral drift, and maintaining safety invariants over time. + Solid programming and systems skills (e.g., Python, Go, or similar), and comfort working across stack boundaries when needed. + Ability to operate effectively in a fast-paced, multifaceted environment, with a bias toward action and delivery. Ways to stand out from the crowd: + Experience securing agentic AI systems or LLM applications that use tools, execute code, or take autonomous actions, especially self-evolving agents that modify their own prompts, tools, or workflows. + Hands-on experience with technologies like Kubernetes , containers, workload isolation, policy engines, and runtime security. + Familiarity with enterprise developer workflows: CI/CD, artifact integrity, dependency/supply-chain security, and secure build pipelines. + Experience designing governance frameworks for emerging technologies-risk tiering, guardrails, rollout playbooks, and adoption enablement. + Background in continuous evaluation pipelines for AI systems, including automated red-teaming, regression testing, or safety benchmarking at scale as well as a s trong intuition for balancing developer productivity with security and compliance , and the ability to build solutions developers actually want to use. ## Description NVIDIA is looking to hire a deeply technical, hands-on Principal Engineer to lead the security foundations for autonomous, self-evolving agents across the enterprise. This engineer is expected to be familiar with agentic AI concepts , sandboxed execution environments , and the security and safety layers required when agents generate and execute code while accessing internal and external data sources . You'll partner closely with Cloud, AI/ML & Generative AI workforce , internal platform teams already building sandboxed environments for LLM-generated code execution , and cross-functional stakeholders including Legal , Security , and Agent Identity teams. Working in a multifaceted and agile environment, you will extend that foundation into a robust safety and security program for long-running, self-improving autonomous agents that refine their own behavior over time, with guardrails enforced at both build time and run time , deep observability and auditing , and continuous evaluation , unblocking teams and setting NVIDIA up for long-term success. What you will be doing: + Lead the end-to-end technical strategy and execution for securing autonomous agents across the enterprise, with a strong bias for enabling developer velocity. + Define agent security and safety requirements and translate them into scalable architectures, guardrails, and platform capabilities as well as e xtend existing sandbox foundations for LLM-generated code execution to support autonomous, tool-using agents and multi-step workflows. + Design and implement strong isolation, policy enforcement, and least-privilege access controls for agent runtimes and tool integrations. + Define and enforce build-time guardrails (policy gates, secure defaults, capability declarations) and run-time guardrails (behavioral boundaries, action allowlists, kill switches) that constrain what self-evolving agents can do as they adapt. + Build secure pathways for agents to access internal and external data sources, including secrets handling, data protection, and governance controls + Establish comprehensive observability and auditing infrastructure (structured logs, decision traces, drift detection, and security telemetry) to ensure agent actions are traceable, measurable, and operationally safe at scale + Design and operate a continuous evaluation framework that benchmarks agent behavior, detects capability drift, and validates that self-improving agents remain within approved safety and security envelopes. + Build a streamlined, developer-friendly experience to run autonomous agents securely-enabling easy onboarding and day-to-day use across both closed-source and open-source agents (e.g., Claude Code, Codex, OpenCode, Openclaw/Claws) with consistent guardrails, policies, and controls. + Drive cross-functional alignment and delivery with Cloud, AI/ML & Generative AI workforce, Legal, Security, Agent Identity, and internal platform teams. + Stay ahead of emerging agent threats and failure modes (particularly risks unique to self-evolving agents), and continuously evolve defenses, standards, and best practices for agent safety and security. ## Related Videos - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Resilient by Design: Building Robust Architectures in High-Stakes Financial Systems](https://www.wearedevelopers.com/videos/2106-resilient-by-design-building-robust-architectures-in-high-stakes-financial-systems) - [Your imaginations is (no longer) the limit: how Generative AI empowers people to be creative](https://www.wearedevelopers.com/videos/741-your-imaginations-is-no-longer-the-limit-how-generative-ai-empowers-people-to-be-creative) - [Building, securing and governing AI infrastructure in the Era of Agentic AI](https://www.wearedevelopers.com/videos/100129-building-securing-and-governing-ai-infrastructure-in-the-era-of-agentic-ai) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) - [Pioneering AI Assistants in Banking](https://www.wearedevelopers.com/videos/1627-pioneering-ai-assistants-in-banking) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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