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.

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.
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.

Software Engineer, DGX Cloud AI Infrastructure

Key skills