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As a Senior System Software Engineer, you will develop features for autonomous driving and robotics by solving complex image and computer vision problems, optimizing software for NVIDIA GPUs, and collaborating on a range of software and hardware solutions from video capturing to rendering in VR.

The Senior System Software Engineer will design and implement scalable, GPU-accelerated frameworks for conversational AI, focusing on Retrieval Augmented Generation (RAG) workflows. The role involves developing AI agent capabilities, analyzing system performance, collaborating with teams on product improvements, and contributing to software development practices.

The Tools and Automation Engineer will develop automated tests and tools for NVIDIA software products, focusing on early defect identification. Responsibilities include executing tests, analyzing results, driving automation initiatives, debugging issues, improving code coverage, and collaborating with development teams.

You will develop and enhance functionalities for Triton Inference Server tools, collaborating with researchers and engineers to analyze performance needs and automate testing processes. Your role includes setting up and analyzing performance for deep learning models, integrating tools, and contributing to technical documentation.

As a Senior Responsible AI Engineer at NVIDIA, you will be evaluating, refining, developing, testing, and automating AI research, practices, and tools to ensure ethically responsible AI models, datasets, and software. You will contribute to shaping NVIDIA's role in ethical AI development by identifying, evaluating, and integrating software tools that promote responsible AI practices.

Join NVIDIA's Software Infrastructure and Operations team as a Senior Engineer. Design and maintain Kubernetes based environments, develop automation tools, deploy new data center infrastructure, and work on cloud infrastructure. Strong background in Kubernetes, programming, databases, and infrastructure management is required. Bachelor's or Master's degree in CS or related field is preferred.

The role involves developing optimized GPU algorithms for data analytics and ML, architecting distributed GPU algorithms for single and multi-node environments, benchmarking solutions, and collaborating with teams to improve integrations with RAPIDS and other high-level frameworks.