Senior Software Engineer - Local AI

NVIDIA•Austin, CA

About The Position

NVIDIA is seeking a Senior Systems Software Engineer to address client-side AI challenges on Windows and Linux PCs with limited resources. This role involves partnering with internal teams and industry partners to advance AI on RTX and DGX PCs, focusing on system-level support for graphics, web browsers, and edge devices. The engineer will improve performance on current and next-generation GPU architectures by optimizing AI models, data processing pipelines, and inference runtime features, and will implement compute and memory optimization techniques for large AI models.

Requirements

Bachelor's, Master's, or PhD in Computer Science, Software Engineering, Mathematics, or a related field (or equivalent experience).
Excellent C++ programming and debugging skills with a strong understanding of data structures and algorithms.
5+ years of experience with proficiency in AI inferencing pipelines and applications using ML/DL frameworks, including ONNX RT, PyTorch, Tensor RT, llama.cpp and vLLM.
Strong analytical and problem-solving abilities, with the ability to multitask effectively in a dynamic environment.
Outstanding written and oral communication skills enabling effective collaboration with management and engineering teams.

Nice To Haves

Understanding modern techniques in Machine Learning, Deep Neural Networks, and Generative AI with relevant contributions to major open-source projects will be a plus.
Consistent track record of delivering end-to-end products with geographically distributed teams in multinational product companies.
Proficiency in lower-level system/GPU programming, CUDA, developing high-performance systems.
Hands-on experience with building applications using APIs like ONNX RT, DirectX, PyTorch, TensorRT, Vulkan, llama.cpp.

Responsibilities

Partnering with NVIDIA software, research, architecture, and product teams to align strategies and technical needs for fostering the ecosystem of AI on RTX and DGX PCs.
Collaborate closely with industry partners to advance AI across critical domains—including graphics, web browsers, and edge devices—by driving innovation in both open and closed source technologies with emphasis on system level support.
Improving performance on current and next-generation GPU architectures by conducting in-depth analysis and end-to-end optimization of AI models, data processing pipelines, and inference runtime features.
Identifying, evaluating, and implementing compute and memory optimization techniques—such as quantization, distillation, and pruning—for large AI models; fine-tuning and compressing models to fit edge devices.