Senior Software Engineer, DGX Cloud AI Infrastructure

NVIDIA Gruppe; Santa Clara (CA); On-site; USD 184,000 - 356,500; Full time

Job summary

NVIDIA Gruppe in Santa Clara is seeking a Senior Software Engineer to lead the optimization of distributed training across large-scale GPU platforms. Candidates should have substantial experience in AI applications and technical leadership.

This role involves profiling end-to-end workloads, debugging complex systems, and delivering actionable insights to drive performance improvements. You will also mentor engineers and define standards that enhance engineering practices.

An inclusive work environment ensures diverse perspectives contribute to our pioneering AI solutions.

Qualifications

• 8+ years of experience developing software for large-scale AI or HPC systems.
• Expertise debugging AI applications from the application layer to hardware.
• Deep hands-on experience with NCCL and multi-GPU workloads.

Responsibilities

• Lead validation and debugging of large-scale AI clusters.
• Benchmark AI workloads using PyTorch and NVIDIA AI software.
• Profile workload performance using tools like Nsight Systems.

Skills

Technical leadership

AI applications debugging

Python programming

C/C++ programming

Analytical skills

Education

Bachelor's or Master's in Computer Science or related field

Tools

NVIDIA software stacks

CUDA

NCCL

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 Senior Software Engineer to lead the bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run.

What you’ll be doing:

• Lead bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads, setting the standard for how the team operates.
• 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.
• Profile and optimize end-to-end workload performance across compute, memory, networking, and communication layers using tools such as Nsight Systems, NCCL tests, and custom microbenchmarks.
• Analyze scaling efficiency for distributed LLM workloads using data, tensor, pipeline, and expert parallelism across modern GPU clusters, and translate findings into concrete tuning guidance.
• Own root-cause analysis of complex failures — hangs, performance regressions, topology sensitivity in large distributed environments.
• Define and build the resilience and failure‑attribution stack: detecting, triaging, and attributing node, fabric, and workload failures across the cluster at scale.
• Build 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.
• Mentor engineers, drive technical standards, and act as a force multiplier across the broader performance and infrastructure organization.

What we need to see:

• Bachelor’s or Master’s in Computer Science or a related technical field (or equivalent experience).
• 8+ years of experience developing software infrastructure for large-scale AI or HPC systems, including a track record of technical leadership.
• Expertise debugging and triaging AI applications across the full stack — from the application layer down to the hardware.
• Deep hands‑on experience with NCCL, CUDA‑aware distributed execution, and debugging multi‑GPU and multi‑node workloads at scale.
• Proven track record of architecting, debugging, and scaling large‑scale distributed systems.
• Expert‑level Python and C/C++ programming skills.
• Experience operating workloads in scheduled, containerized cluster environments.
• Excellent analytical, debugging, and communication skills, with the ability to influence across teams.

Ways to stand out from the crowd:

• Demonstrated experience debugging and optimizing AI workloads at large scale.
• Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric).
• Strong knowledge of GPU cluster fabrics and topology, including NVLink, NVSwitch, PCIe, RoCE, and InfiniBand.
• Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms.
• 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 184,000 USD – 287,500 USD for Level4, and 224,000 USD – 356,500 USD for Level5. You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June8,2026.

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.