NVIDIA
Deep Learning Compiler Engineer
$152K–$242K
US, CA, Santa Clara +5 · Full-time · Posted Aug 23, 2026
- Location
- US, CA, Santa Clara
US, TX, Austin
US, OR, Remote
US, WA, Remote
US, CA, Remote
US, WA, Redmond - Experience
- 3+ years of relevant work or research experience.
- Education
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, or a related field (or equivalent experience).
About the job
Develop CUDA Tile and TensorIR compiler technologies that generate and optimize high-performance GPU kernels across NVIDIA GPU architectures.
What you'll do
- Design compiler transformations, MLIR-based dialects, and lowering passes.
- Optimize tile-based kernel performance across GPU generations.
- Define public APIs and implement compiler, optimization, and general software engineering work.
What they ask for
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, or a related field, or equivalent experience.
- 3+ years of relevant work or research experience in compiler optimization, performance analysis, and IR design.
- Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design.
- Ability to work independently, define project goals and scope, and lead development efforts.
Nice to have
- Knowledge of CPU and/or GPU architecture; CUDA or OpenCL programming experience.
- Experience with MLIR, LLVM, XLA, TVM, and deep learning models and algorithms.
Pay
152,000 USD - 241,500 USD base salary
Tools
CUDA Tile, TensorIR, MLIR, C/C++, CUDA, OpenCL, LLVM, XLA
AI focus
Build compiler technologies that support deep learning workloads; NVIDIA uses AI tools in its recruiting processes.
Model-written summary · the original posting is authoritative