For more than two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics. With our invention of the GPU - the engine of modern visual computing - the field has expanded to PC games, movie production, product design, medical diagnosis, research and AI.

Nowadays, Large Language Models (LLMs) and Generative AI change our world. They help us being productive and collaborative, they fuel our creativity and enable communication across language barriers. Being computationally tremendously demanding, NVIDIA's technology is a driving force behind the wider adoption of these AI models in datacenters and edge computing.

For our team in Wuerselen we are now looking for a Developer Technology Engineer to:

• contribute to the LLM & GenAI open-source ecosystem to enable Windows AI enthusiasts and developers with innovative models and functionality as well as speed-of-light performance on RTX.

• engage with our strategic partners and internal teams to overcome the challenges arising when deploying modern LLM & GenAI architectures on local workstations.

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 GGML, Llama.cpp, Ollama, ONNX Runtime.

• 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 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.

What we need to see:

• 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.

• 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.

Ways to stand out from the crowd:

• Experience with GPU-accelerated AI inference driven by NVIDIA APIs, specifically cuDNN, CUTLASS, TensorRT.

• Confirmed expert knowledge in Vulkan and / or DX12.

• Detailed knowledge of the latest generation GPU architectures.

• Experience with AI deployment on NPUs and ARM architectures.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.