Senior Engineer - AI Agents and Systems

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +1

Senior Software Engineer to build and optimize local AI agent frameworks and inference runtimes on NVIDIA GeForce RTX GPUs for Windows. Focus on performance, security, and hardware integration for consumer PCs.

What you'd actually do

  1. Local Inference Optimization: Optimize performance of local LLMs (Nemotron and others) on GeForce RTX hardware. Profile and optimize inference across Ollama, llama.cpp, and vLLM, minimizing latency and memory footprint using TensorRT and CUDA.
  2. Agent Runtime Engineering: Build and optimize agentic harnesses (NemoClaw, OpenClaw) to run natively and reliably on Windows. Implement the orchestration logic that lets multi-agent systems plan, act, and use tools efficiently on constrained consumer hardware.
  3. Sandboxing & Security: Implement policy-based privacy and security frameworks for autonomous agents, handling filesystem access, secure inference routing, and network egress within thorough sandboxed execution environments.
  4. Hardware/Software Integration: Work close to the metal, integrating agent and inference stacks with NVIDIA's driver and middleware layers to extract maximum performance from RTX GPUs.
  5. Cross-Team Collaboration: Partner with internal AI research teams, driver teams, and the open-source OpenClaw community to ensure our consumer hardware is the best possible platform for local agents.

Skills

Required

  • 12+ years of relevant professional software engineering experience
  • BS, MS, or PhD in Computer Science, Computer Engineering, or a related technical field (or equivalent experience)
  • Hands-on experience with LLM inference pipelines (Ollama, llama.cpp, vLLM)
  • GPU-accelerated computing (CUDA, TensorRT)
  • running local models on consumer-grade hardware
  • Practical experience with modern agentic frameworks (e.g., OpenClaw, LangChain, AutoGPT)
  • working understanding of how multi-agent systems plan, act, and use tools
  • Strong understanding of Windows OS internals, process isolation, sandboxing technologies, and system-level security
  • Proficiency in C++
  • Proficiency in Python
  • Proficiency in TypeScript

Nice to have

  • Demonstrated open-source contributions to AI agent platforms or inference/orchestration tools (especially OpenClaw or llama.cpp)
  • Deep knowledge of NVIDIA GeForce RTX architecture and its specific constraints and advantages for edge AI
  • Experience building virtualization, containerization, or sandboxing tools natively for Windows
  • Active technical community presence (blogs, talks, whitepapers) at the intersection of AI, security, and local compute

What the JD emphasized

  • shipping performance-critical systems
  • LLM inference pipelines
  • local models on consumer-grade hardware
  • agentic frameworks
  • multi-agent systems plan, act, and use tools
  • Windows OS internals
  • sandboxing technologies
  • system-level security
  • C++ (performance-critical systems and OS integration)
  • Python (AI and orchestration logic)
  • TypeScript (agent plugins and tooling)

Other signals

  • local inference optimization
  • agent runtime engineering
  • sandboxing & security for agents
  • GPU optimization for AI