Autonomous Agent Engineer

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking an Autonomous Agent Engineer to build the infrastructure for secure and autonomous AI agent execution. This role involves designing and shipping SDKs, CLIs, and developer tooling for sandboxed compute environments, state management, and security boundaries for AI agents. The position requires strong systems engineering skills, experience with distributed systems and developer platforms, and proficiency in languages like Python or Go. Experience with agentic AI systems, sandboxing technologies, and security fundamentals is highly desirable.

What you'd actually do

  1. Architect sandboxed compute environments where agents securely execute code, access tools, and interact with external services
  2. Design and ship SDKs (Python, Go) and CLI tooling for provisioning and managing agent workloads in isolated environments
  3. Create onboarding templates, reference implementations, and CLI workflows that make secure execution the default
  4. Build state management for long-running agent operations, including checkpoint and recovery
  5. Embed security into SDK primitives like isolation policies, secrets injection, network policies, capability declarations, and kill switches

Skills

Required

  • BS or MS in Computer Science, Engineering, or related field (or equivalent experience)
  • 8+ years building distributed systems, infrastructure, or developer platforms at scale
  • Deep systems engineering skills: containers, microVMs, Kubernetes, Linux security primitives
  • Track record of shipping developer SDKs or CLIs that are adopted by multiple teams
  • Experience building agents using various frameworks and harnesses in enterprise context
  • Proficiency in Python, Go, Rust, or similar

Nice to have

  • Experience building execution environments for agentic AI systems or LLM applications that execute code autonomously
  • Experience with sandboxing and isolation technologies (gVisor, Firecracker, Kata Containers, V8 isolates, or similar)
  • Strong security fundamentals: threat modeling, auth, least privilege, secrets management
  • Designed multi-tenant execution platforms, serverless infrastructure, or sandboxed compute at scale
  • Background in durable execution patterns or checkpoint/recovery systems for long-running workloads

What the JD emphasized

  • autonomous agents
  • sandboxed compute environments
  • developer platforms
  • agent builders
  • agent workloads
  • secure execution
  • long-running agent operations
  • security fundamentals
  • agentic AI systems

Other signals

  • autonomous agents
  • developer platforms
  • sandboxed compute environments
  • SDKs and CLIs