Software Engineer, DGX Cloud AI Infrastructure

NVIDIA

. Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads .

Posted 9/30/2026full-timeRemote • United StatesMid-LevelSenior💰 $108,000 - $178,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and debugging large-scale AI clusters and distributed systems, with strong proficiency in Python and C/C++. Capable of delivering data-driven recommendations and building resilience and failure-attribution systems for datacenter-scale infrastructure.

Highest-signal resume keywords

AI Cluster DevelopmentPython ProgrammingC/C++ ProgrammingCUDA-Aware Distributed ExecutionBenchmarking and Performance Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Workload DebuggingMulti-GPU WorkloadsNCCLRDMA Software StackPerformance Jitter DiagnosisBenchmark Harness DevelopmentFailure-Attribution SystemsRoot-Cause AnalysisAutomation WorkflowsCluster Qualification Tooling

Soft Skills

Analytical SkillsCommunication SkillsCollaborative Approach

Tools & Technologies

PyTorchNeMoMegatronTensorRT-LLMInfiniBandRoCE

Industry Keywords

AIHPCDistributed SystemsDatacenter InfrastructureContainerized Environments

Tech Stack

Tools & technologies

Distributed SystemsNode.jsPythonPyTorchC++

About the role

Key responsibilities & impact

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 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 with framework, systems, and platform teams
Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization

Requirements

What you’ll need

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 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

Comp & perks

Equity
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