Senior Engineer - AI Agents and Systems

NVIDIA · Semiconductors · Santa Clara, CA +1

Senior Engineer role focused on deploying advanced AI agent frameworks and local runtimes to Windows and NVIDIA GeForce RTX GPUs, ensuring open-source AI agents run locally, safely, and efficiently on consumer PCs. The role involves leading development for the foundation of the desktop AI operating system by combining local inference with robust privacy routers and sandboxed execution.

What you'd actually do

  1. Act as the lead engineer for developing the agent frameworks natively on Windows environments. You will shape the technical roadmap to bring always-on, self-evolving AI assistants to GeForce RTX PCs and laptops.
  2. Lead the engineering efforts to optimize the agent runtimes for Windows. You will ensure that autonomous agents operate within thorough, policy-based privacy and security frameworks (e.g., handling filesystem access, secure inference routing, and network egress).
  3. Partner closely with internal AI research teams, driver teams, and the open-source OpenClaw community. Ensure our consumer hardware provides an excellent ecosystem for autonomous agents.
  4. Foster a collaborative engineering culture by mentoring other engineers, establishing best practices for AI agent deployment, and writing reliable, production-ready code.

Skills

Required

  • 10+ years of relevant professional software engineering experience
  • 3+ years in Staff, or Lead Architect role
  • BS, MS, or PhD in Computer Science, Computer Engineering, or a related technical field (or equivalent experience)
  • Deep understanding of Windows OS internals, process isolation, sandboxing technologies, and system-level security architecture
  • Proven understanding of LLM inference pipelines (Ollama, Llamacpp, vLLM)
  • GPU-accelerated computing (CUDA, TensorRT)
  • Experience running local models on consumer-grade hardware
  • Practical experience with modern AI orchestration and agentic frameworks (e.g., OpenClaw, Hermes, LangChain)
  • Understanding of how multi-agent systems plan, act, and use tools
  • Proficiency in C++
  • Proficiency in Python
  • Experience building virtualization, containerization, or robust sandboxing tools natively for the Windows ecosystem

Nice to have

  • AI research teams
  • driver teams
  • open-source OpenClaw community

What the JD emphasized

  • lead engineer
  • Lead the engineering efforts
  • Deep understanding of Windows OS internals, process isolation, sandboxing technologies, and system-level security architecture.
  • Proven understanding of LLM inference pipelines
  • Practical experience with modern AI orchestration and agentic frameworks

Other signals

  • Deploying advanced AI agent frameworks
  • Local AI agents on consumer PCs
  • Desktop AI operating system foundation