Senior DL Simulation Engineer: MLIR & GPU Architectures

NVIDIA; Austin (TX); On-site; USD 152,000 - 288,000; Full time

Job summary

NVIDIA is seeking a Deep Learning Software Engineer to help develop full stack simulation infrastructure for AI workloads. You will work on simulation backends and optimization of compiler kernels, enabling rapid evaluation of AI workloads across compiler stacks.

Ideal candidates have a Masters or PhD in a related field, strong MLIR experience, and proficient C/C++ and Python skills. You’ll collaborate with architects and software teams to guide future GPU features and system design, with

Qualifications

• Masters (or equivalent) in Computer Science, Computer Engineering, or related STEM field; PhD preferred.
• 3+ years of relevant experience in compiler optimization, architectural simulation, or related areas.
• Strong hands-on experience with MLIR and compiler infrastructure.
• Excellent C/C++ and Python programming skills, including software design, debugging, performance analysis, and test development.

Responsibilities

• Develop simulation backends enabling fast, scalable evaluation of AI workloads across NVIDIA compiler stacks.
• Improve deep learning compiler kernel code generation and graph optimization.
• Advance modeling and optimization of datacenter-scale AI workloads and deployment scenarios.
• Partner with architects and software teams to evaluate future GPU features and guide silicon/system-level design decisions.

Skills

MLIR

Compiler infrastructure

C/C++

Python programming

Education

Masters in CS/CE

PhD preferred

Tools

N/A

Job description

NVIDIA is seeking a Deep Learning Software Engineer to help develop full stack simulation infrastructure for AI workloads. You will work on simulation backends and optimization of compiler kernels, enabling rapid evaluation of AI workloads across compiler stacks.

Ideal candidates have a Masters or PhD in a related field, strong MLIR experience, and proficient C/C++ and Python skills. You’ll collaborate with architects and software teams to guide future GPU features and system design, with