NVIDIA is looking for an experienced software engineer with infrastructure experience to become a senior member of the Cloud Foundations Automation - Development Team. We build and manage the automation ecosystem supporting NVIDIA's GPU Cloud and NVIDIA SuperPod deployments. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most hard-working and dedicated people on the planet working for us. If you're creative and autonomous, we want to hear from you! What you'll be doing: Developing software to enable efficient network design, deployment and day 2 management. Building product focused software solutions, used by internal and external customers. Helping us as we transform our workflows and organization into a centrally orchestrated configuration management framework, operating at scale across geographies. Owning and driving integrations with various service APIs such as Cloud Service Providers, to automate creation of environments and auto populate data sources in turn. Building on open source software, designing and implementing data structures and UI interfaces to automate processes from equipment purchase to device config generation to deployment to operations. Streamlining deployment mechanisms and life cycle operations Developing modern service architectures around streaming data and event pipelines. Working with infrastructure domain experts on true, zero touch deployment solutions and utilizing best of breed high performance computing management solutions. Be a proactive problem solver, looking out for new opportunities to improve our services and customer experience. Communicate readily with your peers across the organization, b
Market pay estimate
$230,000–$319,023 / year for comparable Software Engineer roles in United States. Not employer-provided.
Role overview
Job description
NVIDIA is at the forefront of the generative AI revolution, building the software and systems that power the world’s most advanced large language model workloads. 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.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative, autonomous, and love a challenge, we want to hear from you.
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.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Role market pulse
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What they are looking for
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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; 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; Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms; What we need to see: Bachelor’s or Master’s in Computer Science or a related technical field (or equivalent experience)
Hiring company
NVIDIA
We create the world’s fastest supercomputer and largest gaming platform.
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