This role pays less than
68%
of similar roles. Most pay
$196,750–$246,150
— the shaded band above.
At the midpoint, this role pays about
$197k
versus about
$221k
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
942 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 931 roles with salary data.
As a Senior Deep Learning Performance Architect at NVIDIA, you will join a dynamic deep learning architecture team to develop high-performance, energy-efficient system and processor architectures that accelerate machine learning, data analytics, and high-performance computing applications. Your daily tasks include prototyping key algorithms, analyzing trade-offs in performance, cost, and power through analytical models and simulators, and collaborating with software, research, and product teams across the company. To excel in this role, you should have a Master’s or PhD in Computer Science, Electrical Engineering, or Computer Engineering, along with 4+ years of relevant experience. Strong skills in machine learning fundamentals, high-performance power-efficient designs, GPU computing, parallel programming models, and analytical performance modeling are essential. Fluency in Python, C, and C++ is required as you work to identify and resolve performance bottlenecks in deep learning applications.
Python
C
C++
CUDA
MPI
OpenMP
HPC
GPU
Deep Learning
Machine Learning
Performance Modeling
System Architecture
AI Inference
CUDA Kernels
Custom ASIC Hardware
Systems-Level Performance Analysis
Python
C
C++
GPU
CUDA
Parallel Programming
Performance Modeling
Profiling
Deep Learning
Machine Learning
High Performance Computing
Kernel Optimization
Math Libraries
CUTLASS