Senior DevTech Engineer - Windows LLM and GenAI Open-Source Ecosystem

Requirements:

• 5+ years of professional experience in local GPU deployment, profiling, and optimization
• BS or MS degree in Computer Science, Engineering, or related degree
• Strong proficiency in C/C++, Python, software design, programming techniques
• Familiarity with and development experience on the Windows operating system
• Proven theoretical understanding of Transformer architectures - specifically LLMs and Generative AI - and convolutional neural networks
• Experience working with open-source LLM and GenAI software, e.g., PyTorch or llama.cpp
• Experience with CUDA and NVIDIA's Nsight GPU profiling and debugging suite
• Strong verbal and written communication skills in English and organization skills, with a logical approach to problem-solving, time management, and task prioritization skills
• Excellent interpersonal skills
• Some travel is required for conferences and for on-site visits with external partners

What you'll be doing:

• Improve Windows LLM & GenAI user experience on NVIDIA RTX by working on feature and performance enhancements of OSS software, including but not limited to projects like PyTorch, llama.cpp, ComfyUI
• Engage with internal product teams and external OSS maintainers to align on and prioritize OSS enhancements
• Work closely with internal engineering teams and external app developers on solving local end-to-end LLM & Generative AI GPU deployment challenges, using techniques like quantization or distillation
• Apply powerful profiling and debugging tools for analyzing the most demanding GPU-accelerated end-to-end AI applications to detect insufficient GPU utilization resulting in suboptimal runtime performance
• Conduct hands-on trainings, develop sample code and host presentations to give good guidance on efficient end-to-end AI deployment targeting optimal runtime performance
• Guide developers of AI applications applying methodologies for efficient adoption of DL frameworks targeting maximal utilization of GPU Tensor Cores for the best possible inference performance
• Collaborate with GPU driver and architecture teams as well as NVIDIA research to influence next-generation GPU features by providing real-world workflows and giving feedback on partner and customer needs

Nice to haves:

• Experience with GPU-accelerated AI inference driven by NVIDIA APIs, specifically cuDNN, CUTLASS, TensorRT
• Confirmed expert knowledge in Vulkan and / or DX12
• Familiarity with WSL2, Docker
• Detailed knowledge of the latest generation GPU architectures
• Experience with AI deployment on NPUs and ARM architectures

Perks and Benefits:

• Highly competitive salaries
• Extensive benefits package
• Work environment that promotes diversity, inclusion, and flexibility
• Equal opportunity employer committed to fostering a supportive and empowering workplace for all