> Markdown version of [/jobs/ext/1453720-senior-engineer-local-ai-agents-and-systems](https://www.wearedevelopers.com/jobs/ext/1453720-senior-engineer-local-ai-agents-and-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Engineer, Local AI - Agents and Systems - **Company:** NVIDIA Ltd. - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $184,000.0 - $287,500.0 - **Contract:** Permanent contract - **Skills:** Microsoft Windows, Artificial Intelligence, C++ (Programming Language), Cyber Security, Nvidia CUDA, Computer Engineering, Python (Programming Language), Open Source Technology, Software Engineering, Alwayson, Graphics Processing Unit (GPU), Delivery Pipeline, Large Language Models, Multi-Agent Systems, Containerization, Information Technology, Production Code, TensorRT, Virtual Agents - **Published:** July 26, 2026 - **Apply:** https://www.juju.com/job/00000000gjgx85 ## About the Role + 10+ years of relevant professional software engineering experience, with at least 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), and experience running local models on consumer-grade hardware. + Practical experience with modern AI orchestration and agentic frameworks (e.g., OpenClaw, Hermes, LangChain) and an understanding of how multi-agent systems plan, act, and use tools. + Proficiency in multiple languages, particularly C++ (for performance-critical systems/OS integration) and Python (for AI/blueprint logic). + Experience building virtualization, containerization, or robust sandboxing tools natively for the Windows ecosystem. ## Description Artificial intelligence is shifting from passive help to autonomous, always-on workflows. Our mission is to make this change seamless, efficient, and secure for millions globally. We seek a Senior Engineer to lead technical efforts in deploying advanced AI agent frameworks and local runtimes on Windows and NVIDIA GeForce RTX GPUs. You will guide development so open-source AI agents (such as Nemoclaw and OpenClaw) operate locally, safely, and efficiently on consumer PCs. By combining powerful local inference (Nemotron models) with strong privacy routers and sandboxed execution, you will help develop the foundation of the desktop AI operating system. What You Will Be Doing: + 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. + 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). + 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. + Foster a collaborative engineering culture by mentoring other engineers, establishing guidelines for AI agent deployment, and writing reliable, production-ready code. ## Related Videos - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Nemotron: NVIDIA's open model strategy for developers](https://www.wearedevelopers.com/videos/100064-nemotron-nvidia-s-open-model-strategy-for-developers) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Localized Open Models in Production: What Builders Need to Know](https://www.wearedevelopers.com/videos/100270-localized-open-models-in-production-what-builders-need-to-know) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [A 5-Step Open-Source Setup for Agentic Engineering](https://www.wearedevelopers.com/magazine/738-a-5-step-open-source-setup-for-agentic-engineering) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care)