Senior System Software Engineer - Local AI

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About the role

NVIDIA has continuously reinvented itself for over two decades. The invention of the GPU in 1999 fueled the growth of PC gaming, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning has ignited modern AI, positioning NVIDIA as a leading AI computing company.  There is an increasing focus on delivering AI models locally, closer to the source of data. This reduces latency, improves real-time processing, and addresses privacy concerns by minimizing data transfer to centralized servers. As technology advances, client-side AI (local execution) will play a key role in shaping digital experiences. The LocalAI team is seeking a Systems Software Engineer to build efficient on-device AI software for RTX and DGX-class systems. This role focuses on high-performance local inference, low latency, efficient memory use, infrastructure and practical deployment on resource-constrained platforms.  What you'll be doing: • Partnering with NVIDIA's software, research, architecture, and product teams to align strategies and technical needs, encouraging the ecosystem of AI on RTX and DGX PCs.  • Building and optimizing local AI inference stack for RTX, RTX Pro and DGX GPUs, focusing on performance, stability, and scalability across various hardware architectures.  • Architecture and development of modern inference runtimes and execution stacks, covering frameworks like Llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT-RTX across LLMs, vision-language, TTS, ASR, and diffusion AI workloads.  • Perform end-to-end optimization of AI models, data pipelines, and inference runtimes to enhance performance across current and next-generation GPU architectures. Apply model optimization techniques such as quantization, pruning, sparsity, and distillation to enable efficient deployment of large models on local and edge devices.   • Perform system-level debugging, performance optimization, and performance–accuracy trade-off analysis; develop infrastructure for performance and accuracy sweeps, analyze results to identify gaps, and drive fixes; and establish engineering guidelines to accelerate bring-up and ensure production readiness of new models and inference backends.   What we need to see :   • 2+ years of experience with 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, algorithms and machine learning.  • Proven experience working with AI inferencing pipelines and applications using ML/DL frameworks, such as Llama.cpp, vLLM, PyTorch, WinML, DXCGC and TensorRT.  • Deep interest in inference backends and runtime internals, including scheduling, memory management, KV-cache behavior, graph execution, quantization, and hardware-aware optimization.  • Strong analytical and problem-solving abilities, with the capability to multitask effectively in a dynamic environment.  • Outstanding written and oral communication skills, facilitating effective collaboration with management and engineering teams.  Ways to stand out from the crowd:   • Understanding of modern techniques in Machine Learning, Deep Neural Networks, and Generative AI, with relevant contributions to major open-source projects.  • 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, and developing high-performance systems.  • Contributions to open-source inference runtimes, model tooling, or performance infrastructure.  • Hands-on experience building applications with frameworks and APIs like Llama.cpp, PyTorch, TensorRT

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