Senior System Software Engineer, Performance - CUDA Driver

Nvidia

Confirmed live 2 days ago High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Posted: 9 days ago
Freshness: Confirmed live 2 days ago
Closes: Nov 1, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $197k

This role $236k

$136k most similar roles pay here $304k

This role pays more than 83% of similar roles. Most pay $158,225–$235,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $197k 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 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior System Software Engineer, Performance - CUDA Driver

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As a Senior System Software Engineer, Performance - CUDA Driver, you will join the team responsible for developing performance-critical CUDA Driver features and systems software improvements to enhance capabilities for AI, deep learning, and HPC applications. You will diagnose complex performance problems through workload analysis, measurement, and modeling while designing and shipping production features across the software and hardware stack. Your daily work involves optimizing critical CUDA primitives, memory management, CPU–GPU coordination, and interconnect paths for latency and throughput. You will utilize C, C++, and Python to solve bottlenecks in a complex codebase involving operating systems, concurrency, and computer architecture. The role focuses on solving performance challenges at the boundaries of systems software, CPUs, and GPUs, ensuring that CUDA-powered applications achieve maximum efficiency across current and emerging hardware platforms through rigorous engineering and cross-layer investigation.

What does a System Software Engineer earn in California?

Median $235750 from 37 postings across 4 companies.

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Skills

CUDA C++ C GPU Kernel Software performance-engineering Computer Architecture Operating Systems Concurrency Memory Management Python HPC Deep Learning Profiling Firmware Hardware/Software Co-design Pre-silicon Analysis

What you'll do

Develop and ship performance-centric CUDA Driver features and programming-model capabilities from design through validation.
Diagnose complex performance problems using workload analysis, measurement, modeling, and cross-layer root-cause isolation.
Optimize critical CUDA primitives, memory management, CPU–GPU coordination, and interconnect paths for latency and throughput.
Establish performance goals for current and future platforms to drive performance readiness through product releases.
Translate workload and platform evidence into recommendations for CUDA APIs, programming models, and GPU architectures.
Set subsystem performance direction and mentor engineers tackling complex systems and performance challenges.
Influence technical decisions by providing clear performance evidence and conducting rigorous design and code reviews.

What we're looking for

A BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field is required.
Candidates must have at least 7 years of relevant systems-software development experience.
Strong production C/C++ systems-programming experience in complex codebases is required.
Deep knowledge of operating systems and concurrency foundations including threads, synchronization, and memory management is required.
Strong computer architecture foundations regarding processors, memory hierarchy, caching, and system interconnects are required.
Demonstrated success in measuring behavior, identifying bottlenecks, and implementing optimizations to improve software performance is required.
Experience with GPU or accelerator drivers, runtimes, firmware, or compilers is preferred.
Proficiency in Python or other scripting languages for data analysis and experimentation is preferred.

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