Software Engineer, DGX Cloud AI Infrastructure

NVIDIA•Redmond, WA

1d•$116,000 - $224,250

About The Position

NVIDIA is seeking a Software Engineer focused on bringing up, triaging, benchmarking, analyzing, and optimizing distributed training and inference workloads across NVIDIA GPU platforms at large scale. The role involves hands-on work with deep learning systems, GPU performance, distributed computing, and large-scale operations. The engineer will bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and design and implement the benchmarking tools, automation, and debugging workflows that support them. The role also partners with framework, systems, and platform teams to tune workloads and deployments, and provides data-driven recommendations based on profiling, benchmark results, and cluster characterization.

Requirements

Bachelor’s or Master’s degree in Computer Science or a related technical field, or equivalent experience.
At least 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.
Background in 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 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.

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