below marketmost similar roles pay hereabove market
This role pays more than
68%
of similar roles. Most pay
$174,081–$246,150
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$210k
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
1655 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1061 roles with salary data.
The Senior High-Performance LLM Training Engineer joins the team to improve the efficiency of large language model training workloads. This role focuses on optimizing the high-performance software stack for training on thousands of GPUs while helping shape hardware roadmaps for next-generation processors. Key responsibilities include analyzing, profiling, and optimizing AI training workloads, implementing production-quality software across the deep learning platform stack, and building tools to automate workload analysis. The engineer will also support MLPerf Training benchmark submissions and implement workloads in proprietary processor and system simulators. Required technical skills include proficiency in C++, Python, and CUDA, alongside a deep background in computer architecture, neural networks, and GPU architecture. The work involves solving performance problems across state-of-the-art neural networks within frameworks like PyTorch and JAX.
PyTorch
JAX
CUDA
Python
C++
GPU Architecture
Deep Learning
Neural Networks
Performance Analysis
Performance Modeling
LLM Training
Computer Architecture
Simulators
CUDA
C++
Python
GPU Architecture
Deep Learning
Neural Networks
Performance Modeling
MLPerf Training
Computer Architecture
System Simulators
Performance Analysis
Drivers
DL Frameworks
C++
Python
PyTorch
JAX
CUDA
OpenAI Triton
cuBLAS
cuDNN
cuSOLVER
Parallel Systems
Deep Learning
LLMs
Transformer Engine
MLPerf
Code Optimization
Computer Architecture
Operating Systems
C++
Python
PyTorch
JAX
CUDA
OpenAI Triton
cuBLAS
cuDNN
cuSOLVER
Parallel Systems
Deep Learning
LLMs
Computer Architecture
Code Optimization
Operating Systems
MLPerf
Transformer Engine