Used Tools & Technologies
Not specified
Required Skills & Competences
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.
Security @ 4
TypeScript @ 6
Python @ 6
Communication @ 4
LLM @ 4
CUDA @ 4
GPU @ 4
AI @ 4
vLLM @ 4
TensorRT @ 4
LangChain @ 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 moving from passive assistance to autonomous, always-on agentic workflows. The mission is to make this transition flawless, high-performing, and secure for millions of users worldwide, running natively on the GPUs already in consumer PCs.
Responsibilities
- 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.
- Build and optimize agentic harnesses (NemoClaw, OpenClaw) to run natively and reliably on Windows. Implement orchestration logic enabling multi-agent systems to plan, act, and use tools efficiently on constrained consumer hardware.
- Implement policy-based privacy and security frameworks for autonomous agents, handling filesystem access, secure inference routing, and network egress within sandboxed execution environments.
- Integrate agent and inference stacks with NVIDIA's driver and middleware layers to extract maximum performance from RTX GPUs.
- Collaborate with internal AI research teams, driver teams, and the open-source OpenClaw community to ensure consumer hardware is an optimal platform for local agents.
- Write reliable, production-ready code, contribute to engineering best practices, and raise the technical bar through code reviews and design input.
Requirements
- Experience: 12+ years of professional software engineering experience with a track record of shipping performance-critical systems.
- Education: BS, MS, or PhD in Computer Science, Computer Engineering, or a related technical field, or equivalent experience.
- AI & GPU Infrastructure: Hands-on experience with LLM inference pipelines (Ollama, llama.cpp, vLLM), GPU-accelerated computing (CUDA, TensorRT), and running local models on consumer-grade hardware.
- Agentic Frameworks: Practical experience with modern agentic frameworks (e.g., OpenClaw, LangChain, AutoGPT) and a working understanding of multi-agent planning, acting, and tool usage.
- Systems & OS Knowledge: Strong understanding of Windows OS internals, process isolation, sandboxing technologies, and system-level security.
- Programming Languages: Proficiency in C++ (performance-critical systems and OS integration), Python (AI and orchestration logic), and TypeScript (agent plugins and tooling).
- Communication: Ability to translate complex technical decisions into clear documentation and collaborate effectively across diverse engineering teams.
Ways to stand out
- 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 constraints/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.
Compensation & Benefits
- Base salary ranges provided by level:
- Level 5: 224,000 USD - 356,500 USD
- Level 6: 272,000 USD - 431,250 USD
- You will also be eligible for equity and benefits.
Additional Information
- Applications for this job will be accepted at least until July 10, 2026. This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to fostering an inclusive work environment.