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.
