Principal Software Engineer - Enterprise AI Platform

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Principal Software Engineer to lead security foundations for autonomous, self-evolving agents in an enterprise setting. This role involves defining security requirements, designing scalable architectures with guardrails, implementing isolation and access controls, building secure data access pathways, establishing observability and auditing, and operating a continuous evaluation framework for agent behavior. The goal is to enable developer velocity while ensuring robust safety and security for agents that generate and execute code and access data.

What you'd actually do

  1. Lead the end-to-end technical strategy and execution for securing autonomous agents across the enterprise, with a strong bias for enabling developer velocity.
  2. Define agent security and safety requirements and translate them into scalable architectures, guardrails, and platform capabilities as well as extend existing sandbox foundations for LLM-generated code execution to support autonomous, tool-using agents and multi-step workflows.
  3. Design and implement strong isolation, policy enforcement, and least-privilege access controls for agent runtimes and tool integrations.
  4. 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.
  5. Build secure pathways for agents to access internal and external data sources, including secrets handling, data protection, and governance controls

Skills

Required

  • 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.

Nice to have

  • 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 strong intuition for balancing developer productivity with security and compliance

What the JD emphasized

  • security foundations for autonomous, self-evolving agents
  • security and safety layers required when agents generate and execute code
  • accessing internal and external data sources
  • long-running, self-improving autonomous agents
  • guardrails enforced at both build time and run time
  • deep observability and auditing
  • continuous evaluation

Other signals

  • security foundations for autonomous, self-evolving agents
  • sandboxed execution environments
  • security and safety layers required when agents generate and execute code
  • accessing internal and external data sources
  • long-running, self-improving autonomous agents
  • guardrails enforced at both build time and run time
  • deep observability and auditing
  • continuous evaluation