We are looking for a Software Engineer focused on bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run.
In this role you will help bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and own the design and implementation of the benchmarking tooling, automation, and debugging workflows that support them. This is a hands-on role for an engineer who enjoys deep technical problems across deep learning systems, GPU performance, distributed computing, and large-scale operations.
What you'll be doing:- 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.
What we need to see:- 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.
- Backgroun 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.
Ways to stand out from the crowd:- Hands-on experience with NCCL and CUDA-aware distributed execution.
- Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with 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.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 116,000 USD - 189,750 USD for Level 2, and 140,000 USD - 224,250 USD for Level 3.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 7, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
