> Markdown version of [/jobs/ext/486492-software-engineer-dgx-cloud-ai-infrastructure](https://www.wearedevelopers.com/jobs/ext/486492-software-engineer-dgx-cloud-ai-infrastructure). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, DGX Cloud AI Infrastructure - **Company:** NVIDIA Ltd. - **Location:** Austin, TX, United States - **Experience:** Experienced - **Salary:** $116,000.0 - $224,250.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing, Nvidia CUDA, Computer Programming, Software Debugging, Distributed Systems, InfiniBand, Python (Programming Language), Remote Direct Memory Access, Software Engineering, AI Infrastructure, Pytorch, Large Language Models, Deep Learning, AI Platforms, Information Technology, Data Analytics, TensorRT - **Published:** June 5, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=956ede45ad2b8883 ## About the Role Do you have experience in Triage?, Do you have a Master's degree?, * Bachelor's or Master's in Computer Science or a related technical field (or equivalent experience). * 3+ years of 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. * Backgroun 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. Ways to stand out from the crowd: * 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. ## Description In this role you will help bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and own the design and implementation of the benchmarking tooling, automation, and debugging workflows that support them. This is a hands-on role for an engineer who enjoys deep technical problems across deep learning systems, GPU performance, distributed computing, and large-scale operations. What you'll be doing: * 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. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Your Next AI Needs 10,000 GPUs. Now What?](https://www.wearedevelopers.com/videos/1590-your-next-ai-needs-10-000-gpus-now-what) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)