Senior Engineer, Local AI - Agents and Systems

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1

Senior Engineer to lead technical efforts in deploying advanced AI agent frameworks and local runtimes on Windows and NVIDIA GeForce RTX GPUs, enabling open-source AI agents to operate locally, safely, and efficiently on consumer PCs.

What you'd actually do

  1. Act as the lead engineer for developing the agent frameworks natively on Windows environments. You will build 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 detailed, 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 guidelines 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, Llama.cpp, 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

  • mentoring other engineers
  • establishing guidelines for AI agent deployment

What the JD emphasized

  • lead engineer
  • technical roadmap
  • optimize the agent runtimes
  • policy-based privacy and security frameworks
  • open-source OpenClaw community
  • production-ready code
  • 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