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# Software Engineer, DGX Cloud AI Infrastructure

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $184,000.0 - $287,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Application Layers, Profiling, Nvidia CUDA, Computer Programming, Software Debugging, Distributed Systems, InfiniBand, Python (Programming Language), PCI Express, Remote Direct Memory Access, Software Engineering, AI Platforms, Information Technology, Data Analytics
- **Published:** October 12, 2026
- **Apply:** https://www.juju.com/job/1_ScrapingHub33470-JR2019246

## About the Role

What we need to see: + Bachelor's or Master's in Computer Science or a related technical field (or equivalent experience). + 8+ years of experience developing software infrastructure for large-scale AI or HPC systems, including a track record of technical leadership. + Expertise debugging and triaging AI applications across the full stack - from the application layer down to the hardware. + Deep hands-on experience with NCCL, CUDA-aware distributed execution, and debugging multi-GPU and multi-node workloads at scale. + Proven track record of architecting, debugging, and scaling large-scale distributed systems. + Expert-level Python and C/C++ programming skills. + Experience operating workloads in scheduled, containerized cluster environments. + Excellent analytical, debugging, and communication skills, with the ability to influence across teams.
Ways to stand out from the crowd: + Demonstrated experience debugging and optimizing AI workloads at large scale. + Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric). + Strong knowledge of GPU cluster fabrics and topology, including NVLink, NVSwitch, PCIe, RoCE, and InfiniBand. + Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms. + Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure.

## Description

and workload failures across the cluster at scale. + Build 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. + Mentor engineers, drive technical standards, and act as a force multiplier across the broader performance and infrastructure organization.