Description
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.
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.
Responsibilities:
- Help design and build modular, scalable model optimization software platforms that deliver exceptional user experiences while supporting diverse AI models and optimization techniques.
- Explore, develop, and integrate innovative deep learning optimization algorithms (quantization, speculative decoding, sparsity, distillation, pruning, neural architecture search) into NVIDIA's AI software stack.
- Deploy optimized models into leading open-source inference frameworks and contribute specialized APIs, model-level optimizations, and new features tailored to the latest NVIDIA hardware capabilities.
- Collaborate with NVIDIA teams on model optimization solutions for customer use cases, aiming for optimal end-to-end workflows and balanced accuracy–performance trade-offs.
- Conduct GPU kernel-level profiling to identify and capitalize on hardware and software optimization opportunities.
Requirements:
- Pursuing a Master's or PhD in Computer Science, AI, Applied Math, or a related field.
- Strong deep learning background and hands-on experience with language models.
- Solid foundation in algorithms and programming.
- Proficiency in Python, PyTorch, and modern ML frameworks/tools.
- Good written and verbal communication skills, with the ability to work both independently and collaboratively in a fast-paced environment.
Nice to Have:
- Experience with model optimization and efficiency (e.g., quantization, distillation, pruning).
- Contributions to PyTorch, JAX, vLLM, SGLang, or other machine learning training and inference frameworks.
- Hands-on experience training or fine-tuning generative AI models on large-scale GPU clusters.
- Knowledge of GPU architectures and compilation stacks, and the ability to analyze and debug end-to-end performance.
- Familiarity with NVIDIA’s deep learning SDKs (Model Optimizer, NeMo).
NVIDIA offers highly competitive salaries and a comprehensive benefits package.