Software Engineer, DGX Cloud AI Infrastructure

at Nvidia

📍 Santa Clara, United States

USD 116,000-224,200 per year

MIDDLE SENIOR

✅ On-site

Used Tools & Technologies

Not specified

Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Python @ 6 Distributed Systems @ 3 Communication @ 3 Debugging @ 3 LLM @ 3 PyTorch @ 3 CUDA @ 3 GPU @ 3 Deep Learning @ 3 AI @ 3 InfiniBand @ 2 Profiling @ 3 NCCL @ 3 TensorRT @ 3 HPC @ 5

Details

NVIDIA is building software and systems that power large language model workloads. This role focuses on bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at large scale. The position is hands-on and involves deep technical work across deep learning systems, GPU performance, distributed computing, and large-scale operations.

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 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 partnership with framework, systems, and platform teams.
Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.

Requirements

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.
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

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.

Compensation and benefits

Base salary ranges provided by level:
- Level 2: 116,000 USD - 189,750 USD
- Level 3: 140,000 USD - 224,250 USD
Eligible for equity and benefits (link to NVIDIA benefits in original posting).

Additional information

Applications accepted at least until June 7, 2026.
Posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is an equal opportunity employer committed to an inclusive work environment.