Software Engineer, DGX Cloud AI Infrastructure - New College Grad 2026

NVIDIA; Santa Clara (CA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a Software Engineer to bring up, benchmark, and optimize distributed training and inference workloads across its GPU platforms at large scale. This hands-on role focuses on debugging, benchmarking, and improving multi-node AI workloads using CUDA-enabled stacks.

The ideal candidate will have strong Python/C++ skills, experience with multi-GPU and multi-node deployments, and a track record of debugging distributed systems. Equity and benefits are provided.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

C/C++

Distributed systems

GPU performance

Data-driven analysis

Education

Bachelor’s or Master’s in Computer Science or related

Tools

NCCL

CUDA-aware distributed execution

UCX

libfabric

InfiniBand / RoCE

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).
• 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.

You will also be eligible for equity and benefits.

NVIDIA uses AI tools in its recruiting processes.

This posting is for an existing vacancy.

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.