Senior Software Engineer, DGX Cloud AI Infrastructure

🕒 June 4

🏄 California, Oregon, +2 more states – Remote

🏄 California – Remote 🌲 Oregon – Remote 🤠 Texas – Remote ☕ Washington – Remote

💵 $184k - $356.5k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 44%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Lead bring-up, validation, and debugging of 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 • Profile and optimize end-to-end workload performance across compute, memory, networking, and communication layers using 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 • Own root-cause analysis of complex failures, including hangs, performance regressions, and topology sensitivity in large distributed environments • Define and build the resilience and failure-attribution stack for node, fabric, and workload failures across the cluster • Build repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms • Tune runtime settings, communication parameters, and deployment configurations with framework, systems, and platform teams • Deliver data-driven recommendations based on profiling, benchmark results, and cluster characterization • Mentor engineers, drive technical standards, and influence the broader performance and infrastructure organization

🎯 Requirements

• 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 • 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

🏖️ Benefits

• Equity • Benefits