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

AI in this role

Design, benchmark, and optimize distributed AI training and inference infrastructure for NVIDIA DGX Cloud.

pytorchcudancclpythonctensorrt-llm

distributed-systemsgpu-optimizationinfrastructuremachine-learning-infrastructure

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.

Applications for this job will be accepted at least until October 3, 2026.

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.

How we rate this

Software Engineer, DGX Cloud AI Infrastructure - New College Grad 2026 at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Distributed SystemsGPU OptimizationInfrastructureMachine Learning InfrastructurePyTorchCudaNcclPython

Questions you could be asked

Tell me about a project where distributed systems was part of your work. What did you do?
Tell me about a project where gpu optimization was part of your work. What did you do?
Tell me about a project where infrastructure was part of your work. What did you do?
Tell me about a project where machine learning infrastructure was part of your work. What did you do?
Walk me through how you've used PyTorch in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Distributed Systems, GPU Optimization, Infrastructure, Machine Learning Infrastructure, and PyTorch. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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