Senior System Software Engineer, AI Infrastructure

Nvidia

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $124,000–$195,500 / yr
Posted: 3 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k

This role $160k

$109k most similar roles pay here $268k

This role pays less than 79% of similar roles. Most pay $166,950–$240,437 — the shaded band above. At the midpoint, this role pays about $160k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 931 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 919 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Senior System Software Engineer, AI Infrastructure

Role Posting Log in to save

As a System Software Engineer at NVIDIA, you will join a dynamic team focused on advancing AI infrastructure by evaluating hardware and software solutions to enhance product offerings and customer experience. Your day-to-day responsibilities include running multi-node training and inference jobs, designing benchmark suites, profiling deep-learning workloads, creating educational materials, analyzing competitive products, and presenting live demos at global conferences. You will leverage Python and C++ for AI and HPC tasks while working with technologies such as PyTorch, CUDA, cuDNN, and Triton. This role requires a strong background in software development or tech marketing, hands-on experience with cluster schedulers like Slurm and Kubernetes, and expertise in deep learning architectures and distributed training methods.

Skills

Python C++ Slurm Kubernetes PyTorch CUDA cuDNN TensorRT-LLM Triton NCCL InfiniBand NVLink RoCE Collective-comm libraries HPC DL architectures Distributed training methods GPU architecture CPU architecture

What you'll do

Run multi-node training/inference jobs on large GPU clusters to assess performance and usability.
Design benchmark suites highlighting NVIDIA hardware, networking, and software stacks.
Profile deep-learning workloads to identify bottlenecks and provide optimization guidance.
Produce tutorials, scripts, and whitepapers for customer education and technical press.
Analyze competitive solutions and develop data-driven product positioning strategies.

What we're looking for

3+ years of experience in software development or related technical roles.
BS/MS in Computer Science, Electrical Engineering, or a related field.
Proficiency in Python and C++ for AI and HPC applications.
Hands-on experience with multi-node clusters using Slurm or Kubernetes.
Deep understanding of DL architectures, PyTorch, and distributed training methods.
Expertise in CPU/GPU architecture and CUDA, cuDNN, TensorRT-LLM, Triton, NCCL.

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