Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 7
CUDA @ 4
GPU @ 4
LLM @ 4
LangChain @ 4
Mentoring
Python @ 6
Security @ 7
TensorRT @ 4
vLLM @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
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.
Responsibilities
- 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.
Requirements
- 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.
Benefits
- NVIDIA offers highly competitive salaries and a comprehensive benefits package.
- You will also be eligible for equity and benefits.
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