Senior Software Engineer, DGX Cloud AI Infrastructure

We are looking for a Senior Software Engineer to lead the bring-up, triage, benchmarking, analysis, and optimisation of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run.

In this role, you will set technical direction across communication libraries, model frameworks, and inference/training stacks to ensure state-of-the-art LLM workloads run efficiently and reliably at scale. You will lead deep performance and reliability investigations on multi-GPU and multi-node deployments, define how we benchmark and qualify new platforms, and build the resilience and failure-attribution capabilities that keep large clusters productive.

Key Responsibilities:

• Lead bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads, setting the standard for how the team operates.

• 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.

• Profile and optimise end-to-end workload performance across compute, memory, networking, and communication layers using tools such as Nsight Systems, NCCL tests, and custom microbenchmarks.

• Analyse scaling efficiency for distributed LLM workloads using data, tensor, pipeline, and expert parallelism across modern GPU clusters, and translate findings into concrete tuning guidance.

• Own root-cause analysis of complex failures , hangs, performance regressions, topology sensitivity in large distributed environments.

• Define and build the resilience and failure-attribution stack: detecting, triaging, and attributing node, fabric, 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.

Requirements:

• 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, containerised cluster environments.

• Excellent analytical, debugging, and communication skills, with the ability to influence across teams.