Nvidia NVIDIA SolutionS Architect Intern - CSP & CRISP 

United States, Oregon 

970858909

What you’ll be doing

• ​Drive LLM efficiency:​​ Design and leverage advanced low-precision quantization techniques (INT8, FP8, FP4) to optimize inference performance for customer deployments.

• ​Innovate with frameworks:​​ Simulate, optimize, and extend cutting-edge training & inference frameworks (e.g., vLLM, SGLang, TensorRT-LLM, NeMo, Megatron) within NVIDIA's ecosystem.

• ​Enable new AI capabilities:​​ Integrate and validate next-generation LLM architectures and features within core frameworks to expand NVIDIA's solution offerings.

• ​Tune for peak performance:​​ Conduct rigorous performance analysis and tuning of LLM workloads for optimal execution on cloud and on-premises NVIDIA platforms.

• ​Collaborate on customer solutions:​​ Partner with engineering teams and solution architects to translate customer requirements into high-impact LLM engineering implementations.

What we need to see

• Pursuing a ​MS or PhD​ in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field.

• ​Hands-on experience​ with large language model (LLM) training and/or inference frameworks from project work, research, or prior internships.

• ​Strong proficiency in PyTorch​ and ​Python programming.

• ​Solid foundational understanding​ of:

  • Transformer architectures & core LLM algorithms.

  • Principles and trade-offs of model quantization techniques.

  • Distributed training paradigms (e.g., FSDP, ZeRO, 3D/5D parallelism, RLHF infrastructure).

• A ​link to your GitHub profile or code samples​ is required with your application (demonstrating relevant projects).

Ways to stand out from the crowd

• Demonstrable experience with ​quantization tools and workflows​ (e.g., GPTQ, AWQ, SmoothQuant).

• ​Contributions to relevant Open Source Software projects​ (e.g., vLLM, SGLang, Hugging Face Transformers, PyTorch, DeepSpeed).

• ​Understanding of GPU architecture (CUDA), high-performance computing concepts, and cluster communication libraries​ (e.g., NCCL, MPI).

• Record of ​published research​ in machine learning, NLP, or systems at majorconferences/journals.

• Experience deploying or optimizing workloads on ​NVIDIA GPUs​ and familiarity with ​NVIDIA AI software stacks.