Software Engineer, DGX Cloud AI Infrastructure - New College Grad 2026

Summary

NVIDIA is a technology company building software and systems that power advanced generative AI and large language model workloads. The company is seeking a Software Engineer to bring up, benchmark, debug, analyze, and optimize distributed AI training and inference workloads across large-scale NVIDIA GPU platforms. The role also involves developing benchmarking tools, automation, debugging workflows, and resilience tooling for multi-GPU and multi-node environments.

Responsibilities

• Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads
• Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks
• Perform root-cause analysis of failures in large distributed environments
• Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster
• Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms
• Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams
• Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization

Skills

• Bachelor's or Master's in Computer Science or a related technical field (or equivalent experience)
• Experience developing software for AI, HPC, or systems-level applications
• Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution
• Background with debugging and scaling distributed systems
• Experience debugging and triaging AI applications across the full stack, from the application level toward the hardware
• Experience operating workloads in scheduled, containerized cluster environments
• Excellent analytical, debugging, and communication skills, and a collaborative approach across teams
• Strong Python and C/C++ programming skills
• Hands-on experience with NCCL and CUDA-aware distributed execution
• Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with InfiniBand / RoCE congestion debugging
• Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms, including MLPerf
• Experience diagnosing performance jitter
• Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure

Qualifications

Must Haves

• Bachelor's or Master's in Computer Science or a related technical field (or equivalent experience)
• Experience developing software for AI, HPC, or systems-level applications
• Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution
• Background with debugging and scaling distributed systems
• Experience debugging and triaging AI applications across the full stack, from the application level toward the hardware
• Experience operating workloads in scheduled, containerized cluster environments
• Excellent analytical, debugging, and communication skills, and a collaborative approach across teams
• Strong Python and C/C++ programming skills

Nice to Haves

• Hands-on experience with NCCL and CUDA-aware distributed execution
• Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with InfiniBand / RoCE congestion debugging
• Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms, including MLPerf
• Experience diagnosing performance jitter
• Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure

Benefits

• You will also be eligible for equity
• Benefits