NVIDIA is at the forefront of the generative AI revolution! The Algorithmic Model Optimization Team focuses on optimizing generative AI models such as large language models (LLMs), vision-language models (VLMs), and diffusion models for maximal inference efficiency using techniques ranging from quantization, speculative decoding, sparsity, distillation, pruning to neural architecture search, and automated deployment strategies.
Our work includes conducting applied research to improve model efficiency as well as developing an innovative open-source model optimization platform (NVIDIA Model Optimizer). Our software is used both internally across NVIDIA and externally by research and engineering teams alike developing best-in-class AI models.
We are seeking a GenAI Algorithms Intern to help optimize state-of-the-art generative AI models like LLMs, VLMs, multimodal and diffusion models. In this role, you will design, implement, and productionize model optimization algorithms for inference and deployment on NVIDIA’s latest hardware platforms. The focus is on ease of use, compute and memory efficiency, and achieving the best accuracy–performance tradeoffs through software–hardware co-design.
Your work will span multiple layers of the AI software stack—ranging from algorithm design to integration—within NVIDIA’s ecosystem (Model Optimizer, NeMo/Megatron, TensorRT-LLM) and open-source frameworks (PyTorch, Hugging Face, vLLM, SGLang). You may also dive deeper into GPU-level optimization, including custom kernel development with CUDA and Triton.