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).
- 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.
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 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2.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.Skills Required
- Bachelor's or Master's degree in Computer Science or a related technical field, or equivalent experience
- Experience developing software for AI, high-performance computing, or systems-level applications
- Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution
- Background debugging and scaling distributed systems
- Experience debugging and triaging AI applications across the application-to-hardware stack
- Experience operating workloads in scheduled, containerized cluster environments
- Strong Python and C/C++ programming skills
- Experience with NCCL and CUDA-aware distributed execution
- Familiarity with RDMA software stacks, including NCCL, IB verbs, UCX, and libfabric
- Experience debugging InfiniBand or RoCE congestion
- Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling
- Experience with MLPerf
- Experience diagnosing performance jitter
- Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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