Systems Software Engineer, AI and Cloud - New College Grad 2026

at Nvidia

USD 108,000-195,500 per year

JUNIOR

✅ On-site

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 @ 3 API @ 3 Algorithms @ 6 Cloud Computing Data Structures @ 6 Debugging @ 5 Engineering Management HPC JavaScript @ 5 Kubernetes @ 3 LLM @ 3 Marketing Microservices Python @ 5 RAG TensorRT @ 3 vLLM @ 3

Details

NVIDIA is hiring a Systems Software Engineer focused on AI and cloud computing. You will work with advanced AI models, cloud-native architectures, NVIDIA frameworks, SDKs, and microservices to contribute to NVIDIA products and technologies.

Responsibilities

Evaluate cloud-native, full-stack applications using microservices architecture to support AI use cases with NVIDIA frameworks, SDKs, and microservices.
Design and implement agentic workflows using techniques such as Retrieval-Augmented Generation (RAG) and modern AI models.
Evaluate user experiences and analyze the technical performance of AI solutions, compiling findings into comprehensive reports.
Provide practical product improvement recommendations to senior executives and engineering management.
Collaborate with product, marketing, hardware, software engineering, and QA teams to improve NVIDIA products.
Develop developer-focused content, including tutorials and code samples, demonstrating features in NVIDIA tools and libraries.
Write technical whitepapers and product briefs.
Run technical product demonstrations at industry conferences.

Requirements

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

Preferred Qualifications

Advanced knowledge of large language models (LLMs), modern AI software architecture, and cloud APIs.
Contributions to public-facing technical content and open-source projects.
Expertise deploying LLM inference frameworks such as Triton Inference Server, vLLM, or TensorRT on Kubernetes or edge devices to improve performance.