below marketmost similar roles pay hereabove market
This role pays more than
90%
of similar roles. Most pay
$142,500–$222,625
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$183k
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.
Senior Systems Software Engineer, Workload Systems Engineering joins the Workload Systems Engineering team to help engineering teams run demanding build, simulation, and validation workloads efficiently. The role involves building shared self-service and validation mechanisms, providing diagnostic views across application and host layers, and setting performance benchmarks. You will investigate performance and reliability issues across system boundaries, applying profiling, metrics, traces, and controlled experiments to pinpoint improvements. Key responsibilities include evaluating compute and GPU selection, scheduling, concurrency, and storage data paths. Required skills include over eight years of software engineering experience with production compute infrastructure, distributed systems, or large-scale build systems. Proficiency in Linux and systems programming languages is required, alongside experience with GPU systems, drivers, scheduling, remote execution, or container infrastructure.
Build self-service mechanisms for engineering teams to configure and use infrastructure workflows with minimal manual assistance.
Provide diagnostic views across application, remote-execution, and host layers to identify failure and slowdown origins.
Set performance and reliability benchmarks for representative workloads using profiling, metrics, traces, and controlled experiments.
Optimize compute and GPU selection, scheduling, concurrency, worker configuration, and resource sharing.
Make retry causes, recovery outcomes, and wasted execution capacity visible to users and on-call teams.
Implement and validate infrastructure-level changes with application, build, and remote-execution teams.
Measure the impact of infrastructure changes on turnaround time, stability, usable capacity, and cost.
Convert successful investigations into supported workflows, documentation, and clear customer handoffs.
What we're looking for
Over 8 years of software engineering experience with production compute infrastructure, distributed systems, or large-scale build and test systems.
Hands-on experience debugging complex performance or reliability issues across software and hardware boundaries using logs, metrics, traces, profiling, and experiments.
Proficiency in a systems programming or automation language and experience delivering tested changes into production infrastructure.
In-depth experience with Linux and proficiency in GPU systems, scheduling, remote execution, storage, or container infrastructure.
Ability to turn ambiguous symptoms into clear technical plans and explain results to engineering customers.
A degree in computer science, computer engineering, or a related field, or equivalent experience.
Experience with GPU sharing, CUDA execution, MIG, MPS, or high-performance computing workloads (preferred).
Familiarity with Bazel, remote execution, simulation workloads, or improving high-throughput data paths (preferred).
CUDA
C++
C
GPU
systems-programming
Operating Systems
Computer Architecture
Concurrency
Kernel Software
Drivers
performance-engineering
Python
HPC
Deep Learning
Memory Management
Profiling
Hardware/Software Co-design