Software Engineer, AI Networking- New College Grad 2026

Summary: NVIDIA AI is seeking a new college graduate to join their AI Networking co-design and benchmark R&D team. The candidate will be responsible for building and productizing machine learning tools to optimize AI workloads across GPU and CPU clusters, working on distributed Deep Learning and engaging across multiple software layers. Responsibilities: - Design and implement resource allocation and combinatorial optimization techniques (e.g., reinforcement learning, LLM agents for DSE, Bayesian optimization and other multi-objective optimization techniques) to optimize LLM models at datacenter scale - Research, develop, and deploy AI/ML techniques to optimize large-scale Deep Learning (LLM) training and inference on NVIDIA supercomputers and distributed systems. This includes a focus on high-performance networking and NVIDIA communication libraries - Build and productionize ML-based tools for performance prediction and optimization, with a strong emphasis on networking aspects - Develop and deploy a scalable, reliable data curation pipeline capable of handling complex data types, such as time series and PyTorch model graphs, to effectively support the training of high-performance Machine Learning models - Collaborate across hardware and software teams to deliver valuable performance analysis insights - Lead performance test planning, establish performance targets for new technologies and solutions, and drive efforts to achieve those performance goals Required Qualifications: - Master's degree in Computer Science, Software Engineering, or equivalent experience - Experience applying machine learning techniques to computer architecture and system optimization problems. Desired experience involves leveraging ML at the intersection of at least two of the following areas: HPC, networking, and AI applications - Hands-on experience developing and deploying various learning algorithms (e.g., reinforcement learning, offline RL, supervised learning) to tackle optimization challenges within computer architecture, system design, or networking domains - Proficiency in building and using ML models with leading frameworks such as PyTorch or TensorFlow, or JAX - Proven ability to apply GNNs/transformers-based optimization to PyTorch model graph and Kineto execution traces - Expertise combining knowledge of NVIDIA GPUs, the CUDA library, and deep learning frameworks (TensorFlow/PyTorch) with networking concepts, including collective communication libraries (like NCCL) and protocols (such as RoCE and RDMA) - Strong programming capabilities in Python, Bash, and C++ - A collaborative teammate with effective communication and interpersonal abilities Preferred Qualifications: - In-depth knowledge and experience with machine learning/reinforcement learning and frameworks - Comprehensive understanding of computer architecture, system architecture and networking - Extensive experience in applying machine learning techniques such as GNNs or related graph-based models - Knowledge in PyTorch, CUDA, and NCCL libraries - Proven software engineering/development skills Required Skills: Machine Learning, Reinforcement Learning, Supervised Learning, PyTorch, TensorFlow, JAX, Graph Neural Networks, CUDA, NVIDIA GPUs, Collective Communication Libraries, NCCL, RoCE, RDMA, Python, Bash, C++, Computer Architecture, System Optimization, Networking Benefits: Equity, Benefits