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# Systems Software Engineer - AI and Cloud

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, CA, United States
- **Experience:** Experienced
- **Salary:** $62,400.0 - $112,320.0
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, C++ (Programming Language), Cloud Computing, Cloud Engineering, Computer Programming, Computer Engineering, Data Structures, Software Debugging, Python (Programming Language), Open Source Technology, Software Architecture, Software Engineering, High Performance Computing, Large Language Models, Generative AI, Solid Principles, Kubernetes, Information Technology, TensorRT, Api Design, Microservices
- **Published:** August 23, 2026
- **Apply:** https://www.jofdav.com/jobs/59370693-systems-software-engineer-ai-and-cloud

## About the Role

* A Bachelor's or Master's in Software Engineering, Computer Science, Computer Engineering, Electrical Engineering or a related degree (or equivalent experience)
 * 3+ years of experience.
 * Proficiency in Python and JavaScript for programming and debugging, with a strong foundation in data structures, algorithms, and software design principles.
 * Basic familiarity with C++ programming and its application in high-performance computing environments.
 * Experience in crafting cloud-native systems optimized for Kubernetes deployment, using inference frameworks such as vLLM and NVIDIA Triton Inference Server.
 * A solid understanding of API design principles for building scalable, production-ready inference systems.

Ways to stand out from the crowd:

 * Advanced knowledge of LLMs, modern AI software architecture, and cloud APIs.
 * Contributions to public-facing technical content and open-source projects.
 * Expertise in deploying LLM inference frameworks like Triton Inference Server, vLLM, or TensorRT, including on Kubernetes or edge devices to improve performance.

## Description

* Evaluate cloud-native, full-stack applications using microservices architecture to power AI use cases, bringing to bear NVIDIA frameworks, SDKs, and microservices.
 * Design and implement agentic workflows with advanced techniques like Retrieval-Augmented Generation (RAG) and the latest AI models.
 * Evaluate user experiences and analyze the technical performance of AI solutions, compiling findings into comprehensive reports. Offer practical suggestions for product improvement to senior executives and engineering management.
 * Engage with various teams across NVIDIA such as product, marketing, hardware, software engineering, and QA to improve NVIDIA's product offerings.
 * Develop developer-focused content, including detailed tutorials and code samples, to demonstrate the latest features in NVIDIA's tools and libraries.
 * Write technical whitepapers and product briefs, and run technical demos of our products at prominent industry conferences.


## Related Videos

- [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)
- [Your Next AI Needs 10,000 GPUs. Now What?](https://www.wearedevelopers.com/videos/1590-your-next-ai-needs-10-000-gpus-now-what)
- [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way)
- [API Design - Getting Started](https://www.wearedevelopers.com/videos/33-api-design-getting-started)
- [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms)
- [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)

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