below marketmost similar roles pay hereabove market
This role pays more than
79%
of similar roles. Most pay
$196,750–$235,750
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$216k
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
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior System Software Engineer, Performance - CUDA Driver joins the team developing performance-critical CUDA Driver features and systems software improvements. This role involves designing and shipping production features, diagnosing complex performance problems through workload analysis, and delivering validated optimizations for current and emerging platforms. You will optimize critical CUDA primitives, memory management, CPU–GPU coordination, and interconnect paths for latency, throughput, and scalability. The position requires production C/C++ systems-programming experience, strong operating systems foundations including threads and virtual memory, and computer architecture knowledge of processors and memory hierarchies. You will solve performance bottlenecks at the boundaries among systems software, CPUs, interconnects, and GPUs to improve performance for AI, deep learning, and HPC applications. You will also mentor engineers and provide technical influence across hardware and software layers.
What does a System Software Engineer earn in California?
Median $235750 from 70 postings across 4 companies.
Develop and ship performance-centric CUDA Driver features and programming-model capabilities from design through validation.
Diagnose complex performance problems through workload analysis, focused measurement, and cross-layer root-cause isolation.
Optimize CUDA primitives, memory management, CPU–GPU coordination, and interconnect paths for latency and throughput.
Establish performance goals for current and future platforms and characterize new platforms as they come online.
Translate workload and platform evidence into CUDA API, programming model, and future GPU architecture recommendations.
Set subsystem performance direction and mentor engineers tackling complex systems and performance challenges.
Influence technical decisions with clear performance evidence and strengthen implementations through 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, or equivalent practical experience.
At least 7+ years of relevant systems-software development experience.
Strong production C/C++ systems-programming experience, including delivery of substantial features, optimizations, or production fixes in a complex codebase.
Strong operating systems and concurrency foundations, including threads, synchronization, processes, virtual memory, and user/kernel interactions.
Strong computer architecture foundations, including processors, memory hierarchy, caching and coherence, data movement, and system interconnects.
Demonstrated success improving real software performance through measurement, bottleneck identification, and profiling.
Direct CUDA or GPU experience (preferred).
Experience developing GPU/accelerator drivers, runtimes, firmware, compilers, or performance-critical low-level systems (preferred).
CUDA
C
C++
Kernel Mode Drivers
Operating Systems
GPU Architecture
HW/SW Co-design
Virtual Memory
Memory Hierarchy
Interrupts
Memory-Mapped IO
Systems Software Development
Software Verification
High-Performance Computing
CUDA
C
C++
Kernel Mode Drivers
Operating Systems
HW/SW Co-design
Virtual Memory
Threads
Process Control
GPU Architecture
Memory Coherence
Memory-Mapped IO
Software Verification
High-Performance Computing
Artificial Intelligence
CUDA
C
Kernel-mode development
Linux
Parallel computing
Systems software
HW/SW co-design
Performance modeling
Virtual memory
Distributed training
Deep learning frameworks
Memory coherence
Interconnects
Memory-mapped IO
CUDA
C
C++
Kernel Programming
Operating Systems
Parallel Programming
Driver Development
Linux
Windows
macOS
System Architecture
Emulation
Virtual Memory
Memory-Mapped IO
Hardware Architecture