System Software Engineer - Local AI

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.

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.